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1. Abrahamsson, Linda PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_0_j_idt584",{id:"formSmash:items:resultList:0:j_idt584",widgetVar:"widget_formSmash_items_resultList_0_j_idt584",onLabel:"Abrahamsson, Linda ",offLabel:"Abrahamsson, Linda ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:0:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:0:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Statistical models of breast cancer tumour growth for mammography screening data2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis2. Abrahamsson, Per Anders PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_1_j_idt584",{id:"formSmash:items:resultList:1:j_idt584",widgetVar:"widget_formSmash_items_resultList_1_j_idt584",onLabel:"Abrahamsson, Per Anders ",offLabel:"Abrahamsson, Per Anders ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_1_j_idt587",{id:"formSmash:items:resultList:1:j_idt587",widgetVar:"widget_formSmash_items_resultList_1_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Social Sciences, Department of Information Science. Uppsala University, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Social Sciences, Department of Information Science.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:1:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Adami, Hans OlovTaube, AdamKim, KyungMannZelen, MarvinKulldorff, MartinPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:1:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Re: Long-term survival and mortality in prostate cancer treated with noncurative intent1995In: UROLGY, Vol. 154, p. 460-465Article in journal (Refereed)3. Achcar, JA PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_2_j_idt584",{id:"formSmash:items:resultList:2:j_idt584",widgetVar:"widget_formSmash_items_resultList_2_j_idt584",onLabel:"Achcar, JA ",offLabel:"Achcar, JA ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_2_j_idt587",{id:"formSmash:items:resultList:2:j_idt587",widgetVar:"widget_formSmash_items_resultList_2_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Social Sciences, Department of Information Science.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:2:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Agrawal, MCAnand, KNAli, MMAli, MMBagui, SCBaker, RDBalamurali, SBalasooriya, UBansal, AKBarry, JBonett, DGBox, GCarling, KCaudill, SBChakraborti, SChatfield, CChatterjee, SCornell, JACox, DDraper, NREhrenberg, AFinney, DJPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:2:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 25 years of applied statistics1998In: JOURNAL OF APPLIED STATISTICS, ISSN 0266-4763, Vol. 25, no 1, p. 3-22Article in journal (Refereed)4. Adami, Hans-Olov et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_3_j_idt587",{id:"formSmash:items:resultList:3:j_idt587",widgetVar:"widget_formSmash_items_resultList_3_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:3:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Bergström, ReinholdUppsala University, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Social Sciences, Department of Information Science.Weiderpass, ElisabetePersson, IngemarBarlow, LottiMcLaughlin, Joseph K.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:3:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Risk for endometrial cancer following breast cancer: A prospective study in Sweden1997In: Cancer Causes & Control, Vol. 8, p. 821-827Article in journal (Refereed)5. Adami, H-O et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_4_j_idt587",{id:"formSmash:items:resultList:4:j_idt587",widgetVar:"widget_formSmash_items_resultList_4_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:4:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Bergström, RUppsala University, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Social Sciences, Department of Information Science.Engholm, GNyrén, OWolk, AEkbom, AEnglund, ABaron, JPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:4:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); A prospective study of smoking and risk of prostate cancer1996In: Int J Cancer, Vol. 67, p. 764-768Article in journal (Refereed)6. Adami, J PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_5_j_idt584",{id:"formSmash:items:resultList:5:j_idt584",widgetVar:"widget_formSmash_items_resultList_5_j_idt584",onLabel:"Adami, J ",offLabel:"Adami, J ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_5_j_idt587",{id:"formSmash:items:resultList:5:j_idt587",widgetVar:"widget_formSmash_items_resultList_5_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Social Sciences, Department of Information Science.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:5:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Nyren, OBergstrom, REkbom, AMcLaughlin, JKHogman, CFraumeni, JFGlimelius, BPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:5:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Blood transfusion and non-Hodgkin lymphoma: Lack of association1997In: ANNALS OF INTERNAL MEDICINE, ISSN 0003-4819, Vol. 127, no 5, p. 365-&Article in journal (Refereed)7. Adriansson, Nils PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_6_j_idt584",{id:"formSmash:items:resultList:6:j_idt584",widgetVar:"widget_formSmash_items_resultList_6_j_idt584",onLabel:"Adriansson, Nils ",offLabel:"Adriansson, Nils ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_6_j_idt587",{id:"formSmash:items:resultList:6:j_idt587",widgetVar:"widget_formSmash_items_resultList_6_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:6:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Mattsson, IngridUppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:6:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Forecasting GDP Growth, or How Can Random Forests Improve Predictions in Economics?2015Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_6_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:6:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_6_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); GDP is used to measure the economic state of a country and accurate forecasts of it is therefore important. Using the Economic Tendency Survey we investigate forecasting quarterly GDP growth using the data mining technique Random Forest. Comparisons are made with a benchmark AR(1) and an ad hoc linear model built on the most important variables suggested by the Random Forest. Evaluation by forecasting shows that the Random Forest makes the most accurate forecast supporting the theory that there are benefits to using Random Forests on economic time series.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:6:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 8. Ahlberg, Daniel PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_7_j_idt584",{id:"formSmash:items:resultList:7:j_idt584",widgetVar:"widget_formSmash_items_resultList_7_j_idt584",onLabel:"Ahlberg, Daniel ",offLabel:"Ahlberg, Daniel ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_7_j_idt587",{id:"formSmash:items:resultList:7:j_idt587",widgetVar:"widget_formSmash_items_resultList_7_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm Univ, Dept Math, SE-10691 Stockholm, Sweden.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:7:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Griffiths, SimonPUC Rio, Dept Matemat, BR-22451900 Gavea, RJ, Brazil.Janson, SvanteUppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Probability Theory.Morris, RobertInst Nacl Matemat Pura & Aplicada, Estr Dona Castorina 110, BR-22460320 Rio De Janeiro, Brazil.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:7:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Competition in growth and urns2019In: Random structures & algorithms (Print), ISSN 1042-9832, E-ISSN 1098-2418, Vol. 54, no 2, p. 211-227Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_7_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:7:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_7_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We study survival among two competing types in two settings: a planar growth model related to two-neighbor bootstrap percolation, and a system of urns with graph-based interactions. In the planar growth model, uncolored sites are given a color at rate 0, 1 or infinity, depending on whether they have zero, one, or at least two neighbors of that color. In the urn scheme, each vertex of a graph G has an associated urn containing some number of either blue or red balls ( but not both). At each time step, a ball is chosen uniformly at random from all those currently present in the system, a ball of the same color is added to each neighboring urn, and balls in the same urn but of different colors annihilate on a one-for-one basis. We show that, for every connected graph G and every initial configuration, only one color survives almost surely. As a corollary, we deduce that in the two-type growth model on Z(2), one of the colors only infects a finite number of sites with probability one. We also discuss generalizations to higher dimensions and multi-type processes, and list a number of open problems and conjectures.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:7:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 9. Ahlberg, Daniel PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_8_j_idt584",{id:"formSmash:items:resultList:8:j_idt584",widgetVar:"widget_formSmash_items_resultList_8_j_idt584",onLabel:"Ahlberg, Daniel ",offLabel:"Ahlberg, Daniel ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_8_j_idt587",{id:"formSmash:items:resultList:8:j_idt587",widgetVar:"widget_formSmash_items_resultList_8_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Probability Theory. Inst Nacl Matemat Pura & Aplicada, Estr Dona Castorina 110, BR-22460320 Rio De Janeiro, Brazil.;Uppsala Univ, Dept Math, SE-75106 Uppsala, Sweden..PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:8:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Steif, Jeffrey E.Univ Gothenburg, Chalmers Univ Technol, Math Sci, SE-41296 Gothenburg, Sweden..Pete, GaborHungarian Acad Sci, Renyi Inst, 13-15 Realtanoda U, H-1053 Budapest, Hungary.;Budapest Univ Technol & Econ, Inst Math, 1 Egry Jozsef U, H-1111 Budapest, Hungary..PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:8:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Scaling limits for the threshold window: When does a monotone Boolean function flip its outcome?2017In: Annales de l'I.H.P. Probabilites et statistiques, ISSN 0246-0203, E-ISSN 1778-7017, Vol. 53, no 4, p. 2135-2161Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_8_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:8:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_8_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Consider a monotone Boolean function f : {0, 1}(n) -> {0, 1} and the canonical monotone coupling {eta(p) : p is an element of [0, 1]} of an element in {0, 1}(n) chosen according to product measure with intensity p is an element of [0, 1]. The random point p is an element of [0, 1] where f (eta(p)) flips from 0 to 1 is often concentrated near a particular point, thus exhibiting a threshold phenomenon. For a sequence of such Boolean functions, we peer closely into this threshold window and consider, for large n, the limiting distribution (properly normalized to be nondegenerate) of this random point where the Boolean function switches from being 0 to 1. We determine this distribution for a number of the Boolean functions which are typically studied and pay particular attention to the functions corresponding to iterated majority and percolation crossings. It turns out that these limiting distributions have quite varying behavior. In fact, we show that any nondegenerate probability measure on R arises in this way for some sequence of Boolean functions.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:8:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 10. Ahlberg, Daniel PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_9_j_idt584",{id:"formSmash:items:resultList:9:j_idt584",widgetVar:"widget_formSmash_items_resultList_9_j_idt584",onLabel:"Ahlberg, Daniel ",offLabel:"Ahlberg, Daniel ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_9_j_idt587",{id:"formSmash:items:resultList:9:j_idt587",widgetVar:"widget_formSmash_items_resultList_9_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Probability Theory. Inst Matematica Pura & Aplicada, Estr Dona Castorina 110, BR-22460320 Rio De Janeiro, Brazil.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:9:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Tassion, VincentUniv Geneva, 2-4 Rue Lievre, CH-1211 Geneva, Switzerland.Teixeira, AugustoInst Matematica Pura & Aplicada, Estr Dona Castorina 110, BR-22460320 Rio De Janeiro, Brazil.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:9:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Sharpness of the phase transition for continuum percolation in R^{2}2018In: Probability theory and related fields, ISSN 0178-8051, E-ISSN 1432-2064, Vol. 172, no 1-2, p. 525-581Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_9_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:9:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_9_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We study the phase transition of random radii Poisson Boolean percolation: Around each point of a planar Poisson point process, we draw a disc of random radius, independently for each point. The behavior of this process is well understood when the radii are uniformly bounded from above. In this article, we investigate this process for unbounded (and possibly heavy tailed) radii distributions. Under mild assumptions on the radius distribution, we show that both the vacant and occupied sets undergo a phase transition at the same critical parameter.c. Moreover, For. <.c, the vacant set has a unique unbounded connected component and we give precise bounds on the one-arm probability for the occupied set, depending on the radius distribution. At criticality, we establish the box-crossing property, implying that no unbounded component can be found, neither in the occupied nor the vacant sets. We provide a polynomial decay for the probability of the one-arm events, under sharp conditions on the distribution of the radius. For. >.c, the occupied set has a unique unbounded component and we prove that the one-arm probability for the vacant decays exponentially fast. The techniques we develop in this article can be applied to other models such as the Poisson Voronoi and confetti percolation.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:9:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 11. Ahlberg, Daniel PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_10_j_idt584",{id:"formSmash:items:resultList:10:j_idt584",widgetVar:"widget_formSmash_items_resultList_10_j_idt584",onLabel:"Ahlberg, Daniel ",offLabel:"Ahlberg, Daniel ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_10_j_idt587",{id:"formSmash:items:resultList:10:j_idt587",widgetVar:"widget_formSmash_items_resultList_10_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Probability Theory. Inst Nacl Matemat Pura & Aplicada, Rio De Janeiro, RJ, Brazil;Stockholm Univ, Dept Math, SE-10691 Stockholm, Sweden;Stockholm Univ, Dept Math, SE-10691 Stockholm, Sweden.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:10:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Tykesson, JohanChalmers Univ Technol, Dept Math, SE-41296 Gothenburg, Sweden;Univ Gothenburg, Gothenburg, Sweden.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:10:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Gilbert´s disc model with geostatical marking2018In: Advances in Applied Probability, ISSN 0001-8678, E-ISSN 1475-6064, Vol. 50, no 4, p. 1075-1094Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_10_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:10:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_10_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We study a variant of Gilbert's disc model, in which discs are positioned at the points of a Poisson process in R-2 with radii determined by an underlying stationary and ergodic random field phi: R-2 -> [0, infinity), independent of the Poisson process. This setting, in which the random field is independent of the point process, is often referred to as geostatistical marking. We examine how typical properties of interest in stochastic geometry and percolation theory, such as coverage probabilities and the existence of long-range connections, differ between Gilbert's model with radii given by some random field and Gilbert's model with radii assigned independently, but with the same marginal distribution. Among our main observations we find that complete coverage of R(2 )does not necessarily happen simultaneously, and that the spatial dependence induced by the random field may both increase as well as decrease the critical threshold for percolation.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:10:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 12. Ahmad, M. Rauf PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_11_j_idt584",{id:"formSmash:items:resultList:11:j_idt584",widgetVar:"widget_formSmash_items_resultList_11_j_idt584",onLabel:"Ahmad, M. Rauf ",offLabel:"Ahmad, M. Rauf ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:11:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:11:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); A homogeneity test of large dimensional covariance matrices under non-normality2018In: Kybernetika (Praha), ISSN 0023-5954, E-ISSN 1805-949X, Vol. 54, no 5, p. 908-920Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_11_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:11:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_11_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); A test statistic for homogeneity of two or more covariance matrices is presented when the distributions may be non-normal and the dimension may exceed the sample size. Using the Frobenius norm of the difference of null and alternative hypotheses, the statistic is constructed as a linear combination of consistent, location-invariant, estimators of trace functions that constitute the norm. These estimators are defined as U-statistics and the corresponding theory is exploited to derive the normal limit of the statistic under a few mild assumptions as both sample size and dimension grow large. Simulations are used to assess the accuracy of the statistic.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:11:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 13. Ahmad, M. Rauf PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_12_j_idt584",{id:"formSmash:items:resultList:12:j_idt584",widgetVar:"widget_formSmash_items_resultList_12_j_idt584",onLabel:"Ahmad, M. Rauf ",offLabel:"Ahmad, M. Rauf ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:12:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:12:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); A significance test of the RV coefficient in high dimensions2019In: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352, Vol. 131, p. 116-130Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_12_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:12:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_12_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The RV coefficient is an important measure of linear dependence between two multivariate data vectors. Using unbiased and computationally efficient estimators of its components, a modification to the RV coefficient is proposed, and used to construct a test of significance for the true coefficient. The modified estimator improves the accuracy of the original and, along with the test, can be applied to data with arbitrarily large dimensions, possibly exceeding the sample size, and the underlying distribution need only have finite fourth moment. Exact and asymptotic properties are studied under fairly general conditions. The accuracy of the modified estimator and the test is shown through simulations under a variety of parameter settings. In comparisons against several existing methods, both the proposed estimator and the test exhibit similar performance to the distance correlation. Several real data applications are also provided.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:12:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 14. Ahmad, M Rauf PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_13_j_idt584",{id:"formSmash:items:resultList:13:j_idt584",widgetVar:"widget_formSmash_items_resultList_13_j_idt584",onLabel:"Ahmad, M Rauf ",offLabel:"Ahmad, M Rauf ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:13:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:13:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); A unified approach to testing mean vectors with large dimensions2018In: AStA Advances in Statistical Analysis, ISSN 1863-8171, E-ISSN 1863-818XArticle in journal (Refereed)15. Ahmad, M Rauf PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_14_j_idt584",{id:"formSmash:items:resultList:14:j_idt584",widgetVar:"widget_formSmash_items_resultList_14_j_idt584",onLabel:"Ahmad, M Rauf ",offLabel:"Ahmad, M Rauf ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:14:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:14:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Generalized tests of correlation for vectors with large dimensions using modified RV coefficient2019Report (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_14_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:14:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_14_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Tests of zero correlation between two or more vectors with large dimension, possibly largerthan the sample size, are considered when the data may not necessarily follow a normal distribution. A single sample case for several vectors is rst proposed, which is then extended tothe common covariance matrix under the assumption of homogeneity across several independentpopulations. The test statistics are constructed using a recently proposed modicationof the RV coecient for high-dimensional vectors. The accuracy of the tests is shown through simulations.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:14:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 16. Ahmad, M Rauf PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_15_j_idt584",{id:"formSmash:items:resultList:15:j_idt584",widgetVar:"widget_formSmash_items_resultList_15_j_idt584",onLabel:"Ahmad, M Rauf ",offLabel:"Ahmad, M Rauf ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:15:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:15:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Location-invariant and non-invariant tests for large dimensional covariance matrices under normality and non-normality2014Report (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_15_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:15:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_15_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Test statistics for homogeneity, sphericity and identity of high-dimensional covariance matrices are presented under a wide variety of very general conditions when the dimension of the vector, $p$, may exceed the sample size, $n_i$, $i = 1, \ldots, g$. First, location-invariant tests are presented under normality assumption, followed by their robustness to normality by replacing the normality assumption with a mild alternative multivariate model. The two types of tests are then presented in non-invariant form, again under normality and non-normality. Tests of homogeneity of covariance matrices in all cases are immediately supplemented by the tests for sphericity and identity of the common covariance matrix under the null hypothesis. Both location-invariant and non-invariant tests are composed of estimators that are defined as $U$-statistics with kernels of different degrees. Hence, the asymptotic theory of $U$-statistics is employed to arrive at the limiting null and alternative distributions of tests for all cases. These limit distributions are derived using a very mild and practically viable set of assumptions mainly on the traces of the unknown covariance matrices. Finally, corrections and improvements of a few other tests are also presented.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:15:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 17. Ahmad, M. Rauf PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_16_j_idt584",{id:"formSmash:items:resultList:16:j_idt584",widgetVar:"widget_formSmash_items_resultList_16_j_idt584",onLabel:"Ahmad, M. Rauf ",offLabel:"Ahmad, M. Rauf ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:16:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:16:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Location-invariant Multi-sample*U*-tests for Covariance Matrices with Large Dimension2017In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 44, no 2, p. 500-523Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_16_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:16:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_16_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); For two or more multivariate distributions with common covariance matrix, test statistics for certain special structures of the common covariance matrix are presented when the dimension of the multivariate vectors may exceed the number of such vectors. The test statistics are constructed as functions of location-invariant estimators defined as U-statistics, and the corresponding asymptotic theory is used to derive the limiting distributions of the proposed tests. The properties of the test statistics are established under mild and practical assumptions, and the same are numerically demonstrated using simulation results with small or moderate sample sizes and large dimensions.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:16:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 18. Ahmad, M. Rauf PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_17_j_idt584",{id:"formSmash:items:resultList:17:j_idt584",widgetVar:"widget_formSmash_items_resultList_17_j_idt584",onLabel:"Ahmad, M. Rauf ",offLabel:"Ahmad, M. Rauf ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:17:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:17:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Location-invariant tests of homogeneity of large-dimensional covariance matrices2017In: Journal of Statistical Theory and Practice, ISSN 1559-8608, E-ISSN 1559-8616, Vol. 11, no 4, p. 731-745Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_17_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:17:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_17_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); A test statistic for homogeneity of two or more covariance matrices of large dimensions is presented when the data are multivariate normal. The statistic is location-invariant and defined as a function of U-statistics of non-degenerate kernels so that the corresponding asymptotic theory is employed to derive the limiting normal distribution of the test under a few mild and practical assumptions. Accuracy of the test is shown through simulations with different parameter settings.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:17:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 19. Ahmad, M. Rauf PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_18_j_idt584",{id:"formSmash:items:resultList:18:j_idt584",widgetVar:"widget_formSmash_items_resultList_18_j_idt584",onLabel:"Ahmad, M. Rauf ",offLabel:"Ahmad, M. Rauf ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:18:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:18:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Multiple comparisons of mean vectors with large dimension under general conditions2019In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 89, no 6, p. 1044-1059Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_18_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:18:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_18_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Multiple comparisons for two or more mean vectors are considered when the dimension of the vectors may exceed the sample size, the design may be unbalanced, populations need not be normal, and the true covariance matrices may be unequal. Pairwise comparisons, including comparisons with a control, and their linear combinations are considered. Under fairly general conditions, the asymptotic multivariate distribution of the vector of test statistics is derived whose quantiles can be used in multiple testing. Simulations are used to show the accuracy of the tests. Real data applications are also demonstrated.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:18:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 20. Ahmad, M. Rauf PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_19_j_idt584",{id:"formSmash:items:resultList:19:j_idt584",widgetVar:"widget_formSmash_items_resultList_19_j_idt584",onLabel:"Ahmad, M. Rauf ",offLabel:"Ahmad, M. Rauf ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:19:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:19:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Testing homogeneity of several covariance matrices and multi-sample sphericity for high-dimensional data under non-normality2017In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 46, no 8, p. 3738-3753Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_19_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:19:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_19_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); A test for homogeneity of g 2 covariance matrices is presented when the dimension, p, may exceed the sample size, n(i), i = 1, ..., g, and the populations may not be normal. Under some mild assumptions on covariance matrices, the asymptotic distribution of the test is shown to be normal when n(i), p . Under the null hypothesis, the test is extended for common covariance matrix to be of a specified structure, including sphericity. Theory of U-statistics is employed in constructing the tests and deriving their limits. Simulations are used to show the accuracy of tests.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:19:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 21. Ahmad, M. Rauf PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_20_j_idt584",{id:"formSmash:items:resultList:20:j_idt584",widgetVar:"widget_formSmash_items_resultList_20_j_idt584",onLabel:"Ahmad, M. Rauf ",offLabel:"Ahmad, M. Rauf ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:20:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:20:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Tests for independence of vectors with large dimension2017Report (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_20_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:20:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_20_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Given a random sample of n iid vectors, each of dimension p and partitioned into b sub- vectors of sizes pi, i = 1;:::;b. Location-invariant and non-invariant test statistics for independence of sub-vectors are presented when pi may exceed n and the distribution need not be normal. The tests are composed of U -statistics based estimators of the Frobenius norm of the di erence between the null and alternative hypotheses. Asymptotic distributions of the tests are provided for n;pi! 1, where their nite-sample performance is demonstrated through simulations. Some related and subsequent tests are brie y described. Relations of the proposed tests to certain multivariate measures are discussed, which are of interest on their own.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:20:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 22. Ahmad, M. Rauf PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_21_j_idt584",{id:"formSmash:items:resultList:21:j_idt584",widgetVar:"widget_formSmash_items_resultList_21_j_idt584",onLabel:"Ahmad, M. Rauf ",offLabel:"Ahmad, M. Rauf ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_21_j_idt587",{id:"formSmash:items:resultList:21:j_idt587",widgetVar:"widget_formSmash_items_resultList_21_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:21:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Pavlenko, TatjanaKTH, Stockholm, Sweden.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:21:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); A U-classifier for high-dimensional data under non-normality2018In: Journal of Multivariate Analysis, ISSN 0047-259X, E-ISSN 1095-7243, Vol. 167, p. 269-283Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_21_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:21:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_21_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); A classifier for two or more samples is proposed when the data are high-dimensional and the distributions may be non-normal. The classifier is constructed as a linear combination of two easily computable and interpretable components, the U-component and the P-component. The U-component is a linear combination of U-statistics of bilinear forms of pairwise distinct vectors from independent samples. The P-component, the discriminant score, is a function of the projection of the U-component on the observation to be classified. Together, the two components constitute an inherently bias-adjusted classifier valid for high-dimensional data. The classifier is linear but its linearity does not rest on the assumption of homoscedasticity. Properties of the classifier and its normal limit are given under mild conditions. Misclassification errors and asymptotic properties of their empirical counterparts are discussed. Simulation results are used to show the accuracy of the proposed classifier for small or moderate sample sizes and large dimensions. Applications involving real data sets are also included.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:21:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 23. Ahmad, M. Rauf PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_22_j_idt584",{id:"formSmash:items:resultList:22:j_idt584",widgetVar:"widget_formSmash_items_resultList_22_j_idt584",onLabel:"Ahmad, M. Rauf ",offLabel:"Ahmad, M. Rauf ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_22_j_idt587",{id:"formSmash:items:resultList:22:j_idt587",widgetVar:"widget_formSmash_items_resultList_22_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:22:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); von Rosen, D.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:22:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Tests for high-dimensional covariance matrices using the theory of U-statistics2015In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 85, no 13, p. 2619-2631Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_22_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:22:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_22_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Test statistics for sphericity and identity of the covariance matrix are presented, when the data are multivariate normal and the dimension, p, can exceed the sample size, n. Under certain mild conditions mainly on the traces of the unknown covariance matrix, and using the asymptotic theory of U-statistics, the test statistics are shown to follow an approximate normal distribution for large p, also when p >> n. The accuracy of the statistics is shown through simulation results, particularly emphasizing the case when p can be much larger than n. A real data set is used to illustrate the application of the proposed test statistics.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:22:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 24. Ahmad, M. Rauf PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_23_j_idt584",{id:"formSmash:items:resultList:23:j_idt584",widgetVar:"widget_formSmash_items_resultList_23_j_idt584",onLabel:"Ahmad, M. Rauf ",offLabel:"Ahmad, M. Rauf ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_23_j_idt587",{id:"formSmash:items:resultList:23:j_idt587",widgetVar:"widget_formSmash_items_resultList_23_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:23:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Von Rosen, DietrichPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:23:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Tests of Covariance Matrices for High Dimensional Multivariate Data Under Non Normality2015In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 44, no 7, p. 1387-1398Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_23_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:23:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_23_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Ahmad et al. (in press) presented test statistics for sphericity and identity of the covariance matrix of a multivariate normal distribution when the dimension, p, exceeds the sample size, n. In this note, we show that their statistics are robust to normality assumption, when normality is replaced with certain mild assumptions on the traces of the covariance matrix. Under such assumptions, the test statistics are shown to follow the same asymptotic normal distribution as under normality for large p, also whenp >> n. The asymptotic normality is proved using the theory of U-statistics, and is based on very general conditions, particularly avoiding any relationship between n and p.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:23:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 25. Ahmady Phoulady, Hady PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_24_j_idt584",{id:"formSmash:items:resultList:24:j_idt584",widgetVar:"widget_formSmash_items_resultList_24_j_idt584",onLabel:"Ahmady Phoulady, Hady ",offLabel:"Ahmady Phoulady, Hady ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:24:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:24:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Brownian Motions and Scaling Limits of Random Trees2011Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis26. Ali, Mohammad PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_25_j_idt584",{id:"formSmash:items:resultList:25:j_idt584",widgetVar:"widget_formSmash_items_resultList_25_j_idt584",onLabel:"Ali, Mohammad ",offLabel:"Ali, Mohammad ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:25:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:25:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Performance of Three Classification Techniques in Classifying Credit Applications Into Good Loans and Bad Loans: A Comparison2015Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_25_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:25:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_25_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The use of statistical classification techniques in classifying loan applications into good loans and bad loans gained importance with the exponential increase in the demand for credit. It is paramount to use a classification technique with a high predictive capacity to ensure the profitability of the business venture.

In this study we aim to compare the predictive capability of three classification techniques: 1) Logistic regression, 2) CART, and 3) random forests. We apply these techniques on German credit data using an 80:20 learning:test split, and compare the performance of the models fitted using the three classification techniques. The probability of default

*p*for each observation in the test set is calculated using the models fitted on the training dataset. Each test set sample_{i}*x*is then classified into a good loan or a bad loan, based on a threshold , such that_{i}*x*bad loan class if_{i}*p*. We chose several thresholds in order to compare the performance of each of the three classification techniques on five model suitability statistics:_{i }>*Accuracy, precision, negative predictive value, recall,*and*specificity.*None of the classifiers turned out to be best at all the five cross-validation statistics. However, logistic regression has the best performance at low probability of default thresholds. On the other hand, for higher thresholds, CART performs best in

*accuracy, precision,*and*specificity*measures, while random forest performs best for*negative predictive value*and*recall*measures.PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:25:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 27. Allander, Erik PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_26_j_idt584",{id:"formSmash:items:resultList:26:j_idt584",widgetVar:"widget_formSmash_items_resultList_26_j_idt584",onLabel:"Allander, Erik ",offLabel:"Allander, Erik ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_26_j_idt587",{id:"formSmash:items:resultList:26:j_idt587",widgetVar:"widget_formSmash_items_resultList_26_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Faculty of Social Sciences, Department of Information Science.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:26:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Bring, JohanGudmundsson, LudvigMattson, StefanOlafsson, OlafurRigner, Karl-GustavSigurgeirsson, BardurTaube, AdamPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:26:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); What is the long term value of multiphasic screening and the initial judgement of benefit?1997In: Scandinavian Journal of Social Medicine, Vol. Suppl 51, p. 1-20Article in journal (Refereed)28. Alm, Sven Erick PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_27_j_idt584",{id:"formSmash:items:resultList:27:j_idt584",widgetVar:"widget_formSmash_items_resultList_27_j_idt584",onLabel:"Alm, Sven Erick ",offLabel:"Alm, Sven Erick ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Mathematics. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics. Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:27:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:27:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Approximation and Simulation of the Distributions of Scan Statistics for Poisson Processes in Higher Dimensions1998In: Extremes, Vol. 1, p. 111-126Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_27_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:27:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_27_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Given a Poisson process in two or three dimensions we are interested in the scan statistic, i.e. the largest number of points contained in a translate of a fixed scanning set restricted to lie inside a rectangular area.

The distribution of the scan statistic is accurately approximated for rectangular scanning sets, using a technique that is also extended to higher dimensions.

The accuracy of the approximation is checked through simulation.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:27:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 29. Alm, Sven Erick PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_28_j_idt584",{id:"formSmash:items:resultList:28:j_idt584",widgetVar:"widget_formSmash_items_resultList_28_j_idt584",onLabel:"Alm, Sven Erick ",offLabel:"Alm, Sven Erick ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Mathematics. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics. Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:28:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:28:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Monotonicity of the difference between median and mean of gamma distributions and of a related Ramanujan sequence2003In: Bernoulli, ISSN 1350-7265, Vol. 9, no 2, p. 351-371Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_28_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:28:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_28_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); For $n\ge0$, let $\lambda_n$ be the median of the $\Gamma(n+1,1)$ distribution. We prove that the sequence $\{\alpha_n=\lambda_n-n\}$ decreases from $\log 2$ to $2/3$ as $n$ increases from 0 to $\infty$. The difference, $1-\alpha_n$, between the mean and the median thus increases from $1-\log 2$ to $1/3$.

This result also proves the following conjecture by Chen \& Rubin about the Poisson distributions: Let $Y_{\mu}\sim\text{Poisson}(\mu)$, and \lambda_n$ be the largest $\mu$ such that $P(Y_{\mu}\le n)=1/2$, then $\lambda_n-n$ is decreasing in $n$.

The sequence $\{\alpha_n\}$ is related to a sequence $\{\theta_n\}$, introduced by Ramanujan, which is known to be decreasing and of the form

$\theta_n=\frac13+\frac4{135(n+k_n)}$, where $\frac2{21}<k_n\le\frac8{45}$. We also show that the sequence $\{k_n\}$ is decreasing.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:28:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 30. Alm, Sven Erick PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_29_j_idt584",{id:"formSmash:items:resultList:29:j_idt584",widgetVar:"widget_formSmash_items_resultList_29_j_idt584",onLabel:"Alm, Sven Erick ",offLabel:"Alm, Sven Erick ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:29:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:29:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); On Measures of Average Degree for Lattices2006In: Combinatorics, Probability and Computing, Vol. 15, no 4, p. 477-488Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_29_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:29:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_29_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The usual definition of average degree for a non-regular lattice has the disadvantage that it takes the same value for many lattices with clearly different connectivity. We introduce an alternative definition of average degree, which better separates different lattices.

These measures are compared on a class of lattices and are analyzed using a Markov chain describing a random walk on the lattice. Using the new measure, we conjecture the order of both the critical probabilities for bond percolation and the connective constants for self-avoiding walks on these lattices.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:29:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 31. Alm, Sven Erick PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_30_j_idt584",{id:"formSmash:items:resultList:30:j_idt584",widgetVar:"widget_formSmash_items_resultList_30_j_idt584",onLabel:"Alm, Sven Erick ",offLabel:"Alm, Sven Erick ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_30_j_idt587",{id:"formSmash:items:resultList:30:j_idt587",widgetVar:"widget_formSmash_items_resultList_30_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Analysis and Probability Theory.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:30:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Deijfen, MariaStockholm Univ, Dept Math, S-10691 Stockholm, Sweden..PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:30:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); First Passage Percolation on \(\mathbb {Z}^2\): A Simulation Study2015In: Journal of statistical physics, ISSN 0022-4715, E-ISSN 1572-9613, Vol. 161, no 3, p. 657-678Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_30_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:30:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_30_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); First passage percolation on is a model for describing the spread of an infection on the sites of the square lattice. The infection is spread via nearest neighbor sites and the time dynamic is specified by random passage times attached to the edges. In this paper, the speed of the growth and the shape of the infected set is studied by aid of large-scale computer simulations, with focus on continuous passage time distributions. It is found that the most important quantity for determining the value of the time constant, which indicates the inverse asymptotic speed of the growth, is , where are i.i.d. passage time variables. The relation is linear for a large class of passage time distributions. Furthermore, the directional time constants are seen to be increasing when moving from the axis towards the diagonal, so that the limiting shape is contained in a circle with radius defined by the speed along the axes. The shape comes closer to the circle for distributions with larger variability.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:30:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 32. Alm, Sven Erick PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_31_j_idt584",{id:"formSmash:items:resultList:31:j_idt584",widgetVar:"widget_formSmash_items_resultList_31_j_idt584",onLabel:"Alm, Sven Erick ",offLabel:"Alm, Sven Erick ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_31_j_idt587",{id:"formSmash:items:resultList:31:j_idt587",widgetVar:"widget_formSmash_items_resultList_31_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:31:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Janson, SvanteUppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.Linusson, SvantePrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:31:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); First critical probability for a problem on random orientations in G(n,p)2014In: Electronic Journal of Probability, ISSN 1083-6489, E-ISSN 1083-6489, Vol. 19, p. 69-Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_31_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:31:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_31_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We study the random graph G (n,p) with a random orientation. For three fixed vertices s, a, b in G(n,p) we study the correlation of the events {a -> s} (there exists a directed path from a to s) and {s -> b}. We prove that asymptotically the correlation is negative for small p, p < C-1/n, where C-1 approximate to 0.3617, positive for C-1/n < p < 2/n and up to p = p(2)(n). Computer aided computations suggest that p(2)(n) = C-2/n, with C-2 approximate to 7.5. We conjecture that the correlation then stays negative for p up to the previously known zero at 1/2; for larger p it is positive.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:31:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 33. Alm, Sven Erick PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_32_j_idt584",{id:"formSmash:items:resultList:32:j_idt584",widgetVar:"widget_formSmash_items_resultList_32_j_idt584",onLabel:"Alm, Sven Erick ",offLabel:"Alm, Sven Erick ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_32_j_idt587",{id:"formSmash:items:resultList:32:j_idt587",widgetVar:"widget_formSmash_items_resultList_32_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Mathematics. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics. Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:32:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Parviainen, RobertUppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Mathematics. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics. Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:32:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Bounds for the connective constant of the hexagonal lattice2004In: J.\ Phys. A: Math. Gen., Vol. 37, p. 549-Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_32_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:32:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_32_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We give improved bounds for the connective constant of the hexagonal lattice. The lower bound is found by using Kesten's method of irreducible bridges and by determining generating functions for bridges on one-dimensional lattices.

The upper bound is obtained as the largest eigenvalue of a certain transfer matrix. Using a relation between the hexagonal and the $(3.12^2)$ lattices, we also give bounds for the connective constant of the latter lattice.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:32:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 34. Alm, Sven Erick PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_33_j_idt584",{id:"formSmash:items:resultList:33:j_idt584",widgetVar:"widget_formSmash_items_resultList_33_j_idt584",onLabel:"Alm, Sven Erick ",offLabel:"Alm, Sven Erick ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_33_j_idt587",{id:"formSmash:items:resultList:33:j_idt587",widgetVar:"widget_formSmash_items_resultList_33_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Mathematics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:33:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Parviainen, RobertPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:33:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Lower and Upper Bounds for the Time Constant of First-Passage Percolation2001Report (Other scientific)35. Almesjö, Fredrik PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_34_j_idt584",{id:"formSmash:items:resultList:34:j_idt584",widgetVar:"widget_formSmash_items_resultList_34_j_idt584",onLabel:"Almesjö, Fredrik ",offLabel:"Almesjö, Fredrik ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:34:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:34:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Regression modeling of cyclotron spare parts consumption2012Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis36. Aly, S. M. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_35_j_idt584",{id:"formSmash:items:resultList:35:j_idt584",widgetVar:"widget_formSmash_items_resultList_35_j_idt584",onLabel:"Aly, S. M. ",offLabel:"Aly, S. M. ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:35:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:35:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); From Moment Explosion To The Asymptotic Behavior Of The Cumulative Distribution For A Random Variable2017In: Theory of Probability and its Applications, ISSN 0040-585X, E-ISSN 1095-7219, Vol. 61, no 3, p. 357-374Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_35_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:35:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_35_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We study the Tauberian relations between the moment generating function (MGF) and the complementary cumulative distribution function of a random variable whose MGF is finite only on part of the real line. We relate the right tail behavior of the cumulative distribution function of such a random variable to the behavior of its MGF near the critical moment. We apply our results to an arbitrary superposition of a CIR process and the time-integral of this process.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:35:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 37. Amiri, Saeid PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:36:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:36:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); A comparison of bootstrap methods for variance estimation2010In: Journal of Statistical Theory and ApplicationsArticle in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_36_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:36:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_36_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This paper presents a comparison of the nonparametric and parametric bootstrapmethods, when the statistic of interest is the sample variance estimator. Conditionswhen the nonparametric bootstrap method of variance performs better than the para-metric bootstrap method are described

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:36:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 38. Amiri, Saeid PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:37:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:37:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); On Resampling for the Contingency Table based on Information EnergyManuscript (preprint) (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_37_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:37:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_37_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The bootstrap method is studied herein for the analysis of categorical data,in particular for the contingency table. The way to carry out atest of association is to bootstrap on the basis of expected values that havealready been ascertained by a few authors. This paper shows the theoreticalapproach of bootstrapping for a contingency table, and the idea which it isbased on has been inspired by the use of the informational energy function.The properties of the proposed tests are illustrated and discussed using MonteCarlo simulations. The paper ends with analytical examples that elucidate the use ofthe proposed tests.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:37:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 39. Amiri, Saeid PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_38_j_idt584",{id:"formSmash:items:resultList:38:j_idt584",widgetVar:"widget_formSmash_items_resultList_38_j_idt584",onLabel:"Amiri, Saeid ",offLabel:"Amiri, Saeid ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:38:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:38:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); On the Application of the Bootstrap: Coefficient of Variation, Contingency Table, Information Theory and Ranked Set Sampling2011Doctoral thesis, comprehensive summary (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_38_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:38:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_38_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This thesis deals with the bootstrap method. Three decades after the seminal paper by Bradly Efron, still the horizons of this method need more exploration. The research presented herein has stepped into different fields of statistics where the bootstrap method can be utilized as a fundamental statistical tool in almost any application. The thesis considers various statistical problems, which is explained briefly below.

**Bootstrap method:**A comparison of the parametric and the nonparametric bootstrap of variance is presented. The bootstrap of ranked set sampling is dealt with, as well as the wealth of theories and applications on the RSS bootstrap that exist nowadays. Moreover, the performance of RSS in resampling is explored. Furthermore, the application of the bootstrap method in the inference of contingency table test is studied.**Coefficient of variation:**This part shows the capacity of the bootstrap for inferring the coefficient of variation, a task which the asymptotic method does not perform very well.**Information theory:**There are few works on the study of information theory, especially on the inference of entropy. The papers included in this thesis try to achieve the inference of entropy using the bootstrap method.PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:38:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); List of papers PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_38_j_idt626",{id:"formSmash:items:resultList:38:j_idt626",widgetVar:"widget_formSmash_items_resultList_38_j_idt626",onLabel:"List of papers",offLabel:"List of papers",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); 1. A comparison of bootstrap methods for variance estimationOpen this publication in new window or tab >>A comparison of bootstrap methods for variance estimation### Amiri, Saeid

PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_0_overlay_some",{id:"formSmash:items:resultList:38:j_idt627:0:overlay:some",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_0_overlay_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_0_overlay_otherAuthors",{id:"formSmash:items:resultList:38:j_idt627:0:overlay:otherAuthors",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_0_overlay_otherAuthors",multiple:true}); 2010 (English)In: Journal of Statistical Theory and ApplicationsArticle in journal (Refereed) Published##### Abstract [en]

This paper presents a comparison of the nonparametric and parametric bootstrapmethods, when the statistic of interest is the sample variance estimator. Conditionswhen the nonparametric bootstrap method of variance performs better than the para-metric bootstrap method are described

##### Keywords

Bootstrap; Nonparametric; Parametric; Kurtosis; Variance.##### National Category

Probability Theory and Statistics##### Identifiers

urn:nbn:se:uu:diva-158976 (URN)PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_0_overlay_j_idt802",{id:"formSmash:items:resultList:38:j_idt627:0:overlay:j_idt802",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_0_overlay_j_idt802",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_0_overlay_j_idt808",{id:"formSmash:items:resultList:38:j_idt627:0:overlay:j_idt808",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_0_overlay_j_idt808",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_0_overlay_j_idt814",{id:"formSmash:items:resultList:38:j_idt627:0:overlay:j_idt814",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_0_overlay_j_idt814",multiple:true}); $(function(){PrimeFaces.cw("OverlayPanel","overlay441925",{id:"formSmash:items:resultList:38:j_idt627:0:j_idt631",widgetVar:"overlay441925",target:"formSmash:items:resultList:38:j_idt627:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});}); 2. On the Resampling of the Unbalanced Ranked Set SampleOpen this publication in new window or tab >>On the Resampling of the Unbalanced Ranked Set Sample### Amiri, Saeid

PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_1_overlay_some",{id:"formSmash:items:resultList:38:j_idt627:1:overlay:some",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_1_overlay_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_1_overlay_otherAuthors",{id:"formSmash:items:resultList:38:j_idt627:1:overlay:otherAuthors",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_1_overlay_otherAuthors",multiple:true}); (English)Manuscript (preprint) (Other academic)##### Abstract [en]

This paper considers the bootstrap approach of the unbalanced Ranked Set Sampling (RSS) method. Herethe sequence bootstrap is used to shift the analysis of the unbalanced RSS method to an analysis ofthe balanced RSS sample, and balanced RSS is also discussed. Here the consequences of differentalgorithms for carrying out resampling are discussed. The proposed methods are studied using Monte Carloinvestigations. Furthermore, the theoretical approach is discussed.

##### Keywords

Bootstrap method; Monte Carlo simulation; Ranked set sample##### National Category

Probability Theory and Statistics##### Research subject

Statistics##### Identifiers

urn:nbn:se:uu:diva-158983 (URN)PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_1_overlay_j_idt802",{id:"formSmash:items:resultList:38:j_idt627:1:overlay:j_idt802",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_1_overlay_j_idt802",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_1_overlay_j_idt808",{id:"formSmash:items:resultList:38:j_idt627:1:overlay:j_idt808",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_1_overlay_j_idt808",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_1_overlay_j_idt814",{id:"formSmash:items:resultList:38:j_idt627:1:overlay:j_idt814",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_1_overlay_j_idt814",multiple:true}); $(function(){PrimeFaces.cw("OverlayPanel","overlay441935",{id:"formSmash:items:resultList:38:j_idt627:1:j_idt631",widgetVar:"overlay441935",target:"formSmash:items:resultList:38:j_idt627:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});}); 3. On the efficiency of bootstrap method into the analysis contingency tableOpen this publication in new window or tab >>On the efficiency of bootstrap method into the analysis contingency table### Amiri, Saeid

### von Rosen, Dietrich

PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_2_overlay_some",{id:"formSmash:items:resultList:38:j_idt627:2:overlay:some",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_2_overlay_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_2_overlay_otherAuthors",{id:"formSmash:items:resultList:38:j_idt627:2:overlay:otherAuthors",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_2_overlay_otherAuthors",multiple:true}); 2011 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 104, no 2, p. 182-187Article in journal (Refereed) Published##### Abstract [en]

The bootstrap method is a computer intensive statistical method that is widely used in performing nonparametric inference. Categorica ldata analysis,inparticular the analysis of contingency tables, is commonly used in applied field. This work considers nonparametric bootstrap tests for the analysis of contingency tables. There are only a few research papers which exploit this field. The p-values of tests in contingency tables are discrete and should be uniformly distributed under the null hypothesis. The results of this article show that corresponding bootstrap versions work better than the standard tests. Properties of the proposed tests are illustrated and discussed using Monte Carlo simulations. This article concludes with an analytical example that examines the performance of the proposed tests and the confidence interval of the association coefficient.

##### Keywords

Association coefficient, Bootstrap method, Chi-squared test, Contingency table, Monte Carlo simulation##### National Category

Probability Theory and Statistics##### Identifiers

urn:nbn:se:uu:diva-158978 (URN)10.1016/j.cmpb.2011.01.007 (DOI)000296945100018 ()PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_2_overlay_j_idt802",{id:"formSmash:items:resultList:38:j_idt627:2:overlay:j_idt802",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_2_overlay_j_idt802",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_2_overlay_j_idt808",{id:"formSmash:items:resultList:38:j_idt627:2:overlay:j_idt808",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_2_overlay_j_idt808",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_2_overlay_j_idt814",{id:"formSmash:items:resultList:38:j_idt627:2:overlay:j_idt814",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_2_overlay_j_idt814",multiple:true}); $(function(){PrimeFaces.cw("OverlayPanel","overlay441926",{id:"formSmash:items:resultList:38:j_idt627:2:j_idt631",widgetVar:"overlay441926",target:"formSmash:items:resultList:38:j_idt627:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});}); 4. An Improvement of the Nonparametric Bootstrap Test for the Comparison of the Coefficient of VariationsOpen this publication in new window or tab >>An Improvement of the Nonparametric Bootstrap Test for the Comparison of the Coefficient of Variations### Amiri, Saeid

### Zwanzig, Silvelyn

PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_3_overlay_some",{id:"formSmash:items:resultList:38:j_idt627:3:overlay:some",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_3_overlay_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_3_overlay_otherAuthors",{id:"formSmash:items:resultList:38:j_idt627:3:overlay:otherAuthors",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_3_overlay_otherAuthors",multiple:true}); 2010 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 9, p. 1726-1734Article in journal (Refereed) Published##### Abstract [en]

In this article, we propose a new test for examining the equality of the coefficient of variation between two different populations. The proposed test is based on the nonparametric bootstrap method. It appears to yield several appreciable advantages over the current tests. The quick and easy implementation of the test can be considered as advantages of the proposed test. The test is examined by the Monte Carlo simulations, and also evaluated using various numerical studies.

##### Keywords

Bootstrap method, Coefficient of variation, Monte Carlo simulation##### National Category

Mathematics##### Identifiers

urn:nbn:se:uu:diva-134888 (URN)10.1080/03610918.2010.512693 (DOI)000282124000004 ()PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_3_overlay_j_idt802",{id:"formSmash:items:resultList:38:j_idt627:3:overlay:j_idt802",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_3_overlay_j_idt802",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_3_overlay_j_idt808",{id:"formSmash:items:resultList:38:j_idt627:3:overlay:j_idt808",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_3_overlay_j_idt808",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_3_overlay_j_idt814",{id:"formSmash:items:resultList:38:j_idt627:3:overlay:j_idt814",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_3_overlay_j_idt814",multiple:true}); $(function(){PrimeFaces.cw("OverlayPanel","overlay374094",{id:"formSmash:items:resultList:38:j_idt627:3:j_idt631",widgetVar:"overlay374094",target:"formSmash:items:resultList:38:j_idt627:3:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});}); 5. Assessing the coefficient of variations of chemical data using bootstrap methodOpen this publication in new window or tab >>Assessing the coefficient of variations of chemical data using bootstrap method### Amiri, Saeid

### Zwanzig, Silvelyn

PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_4_overlay_some",{id:"formSmash:items:resultList:38:j_idt627:4:overlay:some",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_4_overlay_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_4_overlay_otherAuthors",{id:"formSmash:items:resultList:38:j_idt627:4:overlay:otherAuthors",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_4_overlay_otherAuthors",multiple:true}); 2011 (English)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 25, no 6, p. 295-300Article in journal (Refereed) Published##### Abstract [en]

The coefficient of variation is frequently used in the comparison and precision of results with different scales. This work examines the comparison of the coefficient of variation without any assumptions about the underlying distribution. A family of tests based on the bootstrap method is proposed, and its properties are illustrated using Monte Carlo simulations. The proposed method is applied to chemical experiments with iid and non-iid observations.

##### Keywords

bootstrap method, coefficient of variation, Monte Carlo simulation##### National Category

Mathematics##### Identifiers

urn:nbn:se:uu:diva-156490 (URN)10.1002/cem.1350 (DOI)000292542300002 ()PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_4_overlay_j_idt802",{id:"formSmash:items:resultList:38:j_idt627:4:overlay:j_idt802",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_4_overlay_j_idt802",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_4_overlay_j_idt808",{id:"formSmash:items:resultList:38:j_idt627:4:overlay:j_idt808",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_4_overlay_j_idt808",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_4_overlay_j_idt814",{id:"formSmash:items:resultList:38:j_idt627:4:overlay:j_idt814",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_4_overlay_j_idt814",multiple:true}); $(function(){PrimeFaces.cw("OverlayPanel","overlay431786",{id:"formSmash:items:resultList:38:j_idt627:4:j_idt631",widgetVar:"overlay431786",target:"formSmash:items:resultList:38:j_idt627:4:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});}); 6. The Comparison of Entropies using the Resampling MethodOpen this publication in new window or tab >>The Comparison of Entropies using the Resampling Method### Amiri, Saeid

PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_5_overlay_some",{id:"formSmash:items:resultList:38:j_idt627:5:overlay:some",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_5_overlay_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_5_overlay_otherAuthors",{id:"formSmash:items:resultList:38:j_idt627:5:overlay:otherAuthors",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_5_overlay_otherAuthors",multiple:true}); (English)Manuscript (preprint) (Other academic)##### Abstract [en]

This paper discusses the bootstrap test of entropies. Since the comparison of entropies is of prime interestin applied fields, finding an appropriate way to carry out such a comparison is of the utmost importance. This paperpresents how resampling should be performed to obtain an accurate p-value. Although the test using a pair-wise bootstrapconfidence interval has already been dealt with in some works, here the bootstrap tests are studied because it may demand quite adifferent resampling algorithm compared with the confidence interval. Moreover, the multiple test is studied. The proposed testsappear to yield several appreciable advantages. The easy implementation and the power of the proposed test can be considered asadvantages. Here the entropy of discrete and continuous variables is studied. The proposed tests are examined using Monte Carloinvestigations, and also evaluated using various distributions.

##### Keywords

Bootstrap method; Entropy; Jackknife; Monte Carlo investigation; Multiple test##### National Category

Probability Theory and Statistics##### Research subject

Statistics##### Identifiers

urn:nbn:se:uu:diva-159210 (URN)PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_5_overlay_j_idt802",{id:"formSmash:items:resultList:38:j_idt627:5:overlay:j_idt802",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_5_overlay_j_idt802",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_5_overlay_j_idt808",{id:"formSmash:items:resultList:38:j_idt627:5:overlay:j_idt808",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_5_overlay_j_idt808",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_5_overlay_j_idt814",{id:"formSmash:items:resultList:38:j_idt627:5:overlay:j_idt814",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_5_overlay_j_idt814",multiple:true}); $(function(){PrimeFaces.cw("OverlayPanel","overlay443444",{id:"formSmash:items:resultList:38:j_idt627:5:j_idt631",widgetVar:"overlay443444",target:"formSmash:items:resultList:38:j_idt627:5:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});}); 7. On Resampling for the Contingency Table based on Information EnergyOpen this publication in new window or tab >>On Resampling for the Contingency Table based on Information Energy### Amiri, Saeid

PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_6_overlay_some",{id:"formSmash:items:resultList:38:j_idt627:6:overlay:some",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_6_overlay_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_6_overlay_otherAuthors",{id:"formSmash:items:resultList:38:j_idt627:6:overlay:otherAuthors",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_6_overlay_otherAuthors",multiple:true}); (English)Manuscript (preprint) (Other academic)##### Abstract [en]

The bootstrap method is studied herein for the analysis of categorical data,in particular for the contingency table. The way to carry out atest of association is to bootstrap on the basis of expected values that havealready been ascertained by a few authors. This paper shows the theoreticalapproach of bootstrapping for a contingency table, and the idea which it isbased on has been inspired by the use of the informational energy function.The properties of the proposed tests are illustrated and discussed using MonteCarlo simulations. The paper ends with analytical examples that elucidate the use ofthe proposed tests.

##### Keywords

Bootstrap method; Chi-squared test; Contingency table; Informational energy; Monte Carlo investigation##### National Category

Probability Theory and Statistics##### Research subject

Statistics##### Identifiers

urn:nbn:se:uu:diva-158980 (URN)PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_6_overlay_j_idt802",{id:"formSmash:items:resultList:38:j_idt627:6:overlay:j_idt802",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_6_overlay_j_idt802",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_6_overlay_j_idt808",{id:"formSmash:items:resultList:38:j_idt627:6:overlay:j_idt808",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_6_overlay_j_idt808",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_38_j_idt627_6_overlay_j_idt814",{id:"formSmash:items:resultList:38:j_idt627:6:overlay:j_idt814",widgetVar:"widget_formSmash_items_resultList_38_j_idt627_6_overlay_j_idt814",multiple:true}); $(function(){PrimeFaces.cw("OverlayPanel","overlay441929",{id:"formSmash:items:resultList:38:j_idt627:6:j_idt631",widgetVar:"overlay441929",target:"formSmash:items:resultList:38:j_idt627:6:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});}); PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:38:partsPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 40. Amiri, Saeid PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_39_j_idt584",{id:"formSmash:items:resultList:39:j_idt584",widgetVar:"widget_formSmash_items_resultList_39_j_idt584",onLabel:"Amiri, Saeid ",offLabel:"Amiri, Saeid ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:39:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:39:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); On the Resampling of the Unbalanced Ranked Set SampleManuscript (preprint) (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_39_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:39:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_39_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This paper considers the bootstrap approach of the unbalanced Ranked Set Sampling (RSS) method. Herethe sequence bootstrap is used to shift the analysis of the unbalanced RSS method to an analysis ofthe balanced RSS sample, and balanced RSS is also discussed. Here the consequences of differentalgorithms for carrying out resampling are discussed. The proposed methods are studied using Monte Carloinvestigations. Furthermore, the theoretical approach is discussed.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:39:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 41. Amiri, Saeid PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_40_j_idt584",{id:"formSmash:items:resultList:40:j_idt584",widgetVar:"widget_formSmash_items_resultList_40_j_idt584",onLabel:"Amiri, Saeid ",offLabel:"Amiri, Saeid ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:40:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:40:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); The Comparison of Entropies using the Resampling MethodManuscript (preprint) (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_40_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:40:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_40_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This paper discusses the bootstrap test of entropies. Since the comparison of entropies is of prime interestin applied fields, finding an appropriate way to carry out such a comparison is of the utmost importance. This paperpresents how resampling should be performed to obtain an accurate p-value. Although the test using a pair-wise bootstrapconfidence interval has already been dealt with in some works, here the bootstrap tests are studied because it may demand quite adifferent resampling algorithm compared with the confidence interval. Moreover, the multiple test is studied. The proposed testsappear to yield several appreciable advantages. The easy implementation and the power of the proposed test can be considered asadvantages. Here the entropy of discrete and continuous variables is studied. The proposed tests are examined using Monte Carloinvestigations, and also evaluated using various distributions.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:40:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 42. Amiri, Saeid PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_41_j_idt584",{id:"formSmash:items:resultList:41:j_idt584",widgetVar:"widget_formSmash_items_resultList_41_j_idt584",onLabel:"Amiri, Saeid ",offLabel:"Amiri, Saeid ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_41_j_idt587",{id:"formSmash:items:resultList:41:j_idt587",widgetVar:"widget_formSmash_items_resultList_41_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Univ Wisconsin, Dept Nat & Appl Sci, Green Bay, WI 54302 USA..PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:41:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Modarres, RezaGeorge Washington Univ, Dept Stat, Washington, DC 20052 USA..Zwanzig, SilvelynUppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:41:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Tests of perfect judgment ranking using pseudo-samples2017In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 32, no 4, p. 1309-1322Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_41_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:41:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_41_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Ranked set sampling (RSS) is a sampling approach that can produce improved statistical inference when the ranking process is perfect. While some inferential RSS methods are robust to imperfect rankings, other methods may fail entirely or provide less efficiency. We develop a nonparametric procedure to assess whether the rankings of a given RSS are perfect. We generate pseudo-samples with a known ranking and use them to compare with the ranking of the given RSS sample. This is a general approach that can accommodate any type of raking, including perfect ranking. To generate pseudo-samples, we consider the given sample as the population and generate a perfect RSS. The test statistics can easily be implemented for balanced and unbalanced RSS. The proposed tests are compared using Monte Carlo simulation under different distributions and applied to a real data set.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:41:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 43. Amiri, Saeid PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_42_j_idt584",{id:"formSmash:items:resultList:42:j_idt584",widgetVar:"widget_formSmash_items_resultList_42_j_idt584",onLabel:"Amiri, Saeid ",offLabel:"Amiri, Saeid ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_42_j_idt587",{id:"formSmash:items:resultList:42:j_idt587",widgetVar:"widget_formSmash_items_resultList_42_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:42:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); von Rosen, DietrichPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:42:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); On the efficiency of bootstrap method into the analysis contingency table2011In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 104, no 2, p. 182-187Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_42_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:42:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_42_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The bootstrap method is a computer intensive statistical method that is widely used in performing nonparametric inference. Categorica ldata analysis,inparticular the analysis of contingency tables, is commonly used in applied field. This work considers nonparametric bootstrap tests for the analysis of contingency tables. There are only a few research papers which exploit this field. The p-values of tests in contingency tables are discrete and should be uniformly distributed under the null hypothesis. The results of this article show that corresponding bootstrap versions work better than the standard tests. Properties of the proposed tests are illustrated and discussed using Monte Carlo simulations. This article concludes with an analytical example that examines the performance of the proposed tests and the confidence interval of the association coefficient.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:42:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 44. Amiri, Saeid et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_43_j_idt587",{id:"formSmash:items:resultList:43:j_idt587",widgetVar:"widget_formSmash_items_resultList_43_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:43:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); von Rosen, DietrichZwanzig, SilvelynUppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:43:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); The SVM Approach for Box–Jenkins ModelsAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_43_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:43:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_43_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Support Vector Machine (SVM) is known in classification and regression modeling. It has been receiving attention in the application of nonlinear functions. The aim is to motivate the use of the SVM approach to analyze the time series models. This is an effort to assess the performance of SVM in comparison with ARMA model. The applicability of this approach for a unit root situation is also considered.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:43:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 45. Amiri, Saied PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_44_j_idt584",{id:"formSmash:items:resultList:44:j_idt584",widgetVar:"widget_formSmash_items_resultList_44_j_idt584",onLabel:"Amiri, Saied ",offLabel:"Amiri, Saied ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:44:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:44:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); On the Application of the Transformation to Testing the Coeffcient of VariationManuscript (preprint) (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_44_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:44:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_44_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); It is difficult to drive mathematically the theoretical sampling distribution of the coefficientof variation (CV) and to make inferences about it. This paper attempts to provide an overview ofa parametric asymptotic inference of the coefficient of variation using a transformation that gives variancestabilization. Although it can easily be shown that the variance of the logarithm of the sample mean is approximatelythe coefficient of variation, up to now it has not been demonstrated how this idea can be used to draw inferences concerningthe CV. This article shows how the bootstrap method can be used to improve the discussed methods and deals with one- andtwo-sample tests of the CV.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:44:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 46. Anders Lindfors, Roland Roberts, Anders Christoffersson and Gunnar Anderlind PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_45_j_idt584",{id:"formSmash:items:resultList:45:j_idt584",widgetVar:"widget_formSmash_items_resultList_45_j_idt584",onLabel:"Anders Lindfors, Roland Roberts, Anders Christoffersson and Gunnar Anderlind ",offLabel:"Anders Lindfors, Roland Roberts, Anders Christoffersson and Gunnar Anderlind ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:45:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:45:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Model based frequency domain estimation of the thermal properties of building insulation1995In: J. Thermal Insul. and bldg. envs., Vol. 18, no 1, p. 31-Article in journal (Refereed)47. Andersson, Björn PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_46_j_idt584",{id:"formSmash:items:resultList:46:j_idt584",widgetVar:"widget_formSmash_items_resultList_46_j_idt584",onLabel:"Andersson, Björn ",offLabel:"Andersson, Björn ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:46:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:46:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); An Evaluation of Hypothesis Testing Methods for Equating Differences in Kernel EquatingManuscript (preprint) (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_46_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:46:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_46_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); In observed-score equating, hypothesis tests of equating differences are helpful in deciding which equating function is suitable. Here, a hypothesis testing procedure for item response theory (IRT) observed-score kernel equating using a Wald test is introduced. Simulations evaluating the Wald test when using IRT and log-linear models are conducted. The test with either IRT or log-linear models is shown to have high power and greatly outperform the Hommel multiple hypothesis testing method. The Wald test is applied to two datasets in both an equivalent groups design and a non-equivalent groups design, showing that the Wald test can provide different conclusions to other hypothesis testing methods in practice.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:46:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 48. Andersson, Björn PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_47_j_idt584",{id:"formSmash:items:resultList:47:j_idt584",widgetVar:"widget_formSmash_items_resultList_47_j_idt584",onLabel:"Andersson, Björn ",offLabel:"Andersson, Björn ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:47:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:47:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Contributions to Kernel Equating2014Doctoral thesis, comprehensive summary (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_47_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:47:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_47_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The statistical practice of equating is needed when scores on different versions of the same standardized test are to be compared. This thesis constitutes four contributions to the observed-score equating framework kernel equating.

Paper I introduces the open source R package kequate which enables the equating of observed scores using the kernel method of test equating in all common equating designs. The package is designed for ease of use and integrates well with other packages. The equating methods non-equivalent groups with covariates and item response theory observed-score kernel equating are currently not available in any other software package.

In paper II an alternative bandwidth selection method for the kernel method of test equating is proposed. The new method is designed for usage with non-smooth data such as when using the observed data directly, without pre-smoothing. In previously used bandwidth selection methods, the variability from the bandwidth selection was disregarded when calculating the asymptotic standard errors. Here, the bandwidth selection is accounted for and updated asymptotic standard error derivations are provided.

Item response theory observed-score kernel equating for the non-equivalent groups with anchor test design is introduced in paper III. Multivariate observed-score kernel equating functions are defined and their asymptotic covariance matrices are derived. An empirical example in the form of a standardized achievement test is used and the item response theory methods are compared to previously used log-linear methods.

In paper IV, Wald tests for equating differences in item response theory observed-score kernel equating are conducted using the results from paper III. Simulations are performed to evaluate the empirical significance level and power under different settings, showing that the Wald test is more powerful than the Hommel multiple hypothesis testing method. Data from a psychometric licensure test and a standardized achievement test are used to exemplify the hypothesis testing procedure. The results show that using the Wald test can provide different conclusions to using the Hommel procedure.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:47:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); List of papers PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_47_j_idt626",{id:"formSmash:items:resultList:47:j_idt626",widgetVar:"widget_formSmash_items_resultList_47_j_idt626",onLabel:"List of papers",offLabel:"List of papers",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); 1. Performing the Kernel Method of Test Equating with the Package kequateOpen this publication in new window or tab >>Performing the Kernel Method of Test Equating with the Package kequate### Andersson, Björn

### Bränberg, Kenny

Department of Statistics, USBE, Umeå University.### Wiberg, Marie

Department of Statistics, USBE, Umeå University.PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_0_overlay_some",{id:"formSmash:items:resultList:47:j_idt627:0:overlay:some",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_0_overlay_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_0_overlay_otherAuthors",{id:"formSmash:items:resultList:47:j_idt627:0:overlay:otherAuthors",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_0_overlay_otherAuthors",multiple:true}); 2013 (English)In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 55, no 6, p. 1-25Article in journal (Refereed) Published##### Abstract [en]

In standardized testing it is important to equate tests in order to ensure that the test takers, regardless of the test version given, obtain a fair test. Recently, the kernel method of test equating, which is a conjoint framework of test equating, has gained popularity. The kernel method of test equating includes five steps: (1) pre-smoothing, (2) estimation of the score probabilities, (3) continuization, (4) equating, and (5) computing the standard error of equating and the standard error of equating difference. Here, an implementation has been made for six different equating designs: equivalent groups, single group, counterbalanced, non-equivalent groups with anchor test using either chain equating or post-stratification equating, and non-equivalent groups using covariates. An R package for the kernel method of test equating called kequate is presented. Included in the package are also diagnostic tools aiding in the search for a proper log-linear model in the pre-smoothing step for use in conjunction with the R function glm.

##### Place, publisher, year, edition, pages

American Statistical Association, 2013##### Keywords

observed-score test equating, R package, kernel equating, item-response theory##### National Category

Probability Theory and Statistics##### Research subject

Statistics##### Identifiers

urn:nbn:se:uu:diva-208912 (URN)000325948000001 ()PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_0_overlay_j_idt802",{id:"formSmash:items:resultList:47:j_idt627:0:overlay:j_idt802",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_0_overlay_j_idt802",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_0_overlay_j_idt808",{id:"formSmash:items:resultList:47:j_idt627:0:overlay:j_idt808",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_0_overlay_j_idt808",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_0_overlay_j_idt814",{id:"formSmash:items:resultList:47:j_idt627:0:overlay:j_idt814",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_0_overlay_j_idt814",multiple:true}); $(function(){PrimeFaces.cw("OverlayPanel","overlay658862",{id:"formSmash:items:resultList:47:j_idt627:0:j_idt631",widgetVar:"overlay658862",target:"formSmash:items:resultList:47:j_idt627:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});}); 2. Improving the Bandwidth Selection in Kernel EquatingOpen this publication in new window or tab >>Improving the Bandwidth Selection in Kernel Equating### Andersson, Björn

### von Davier, Alina A.

Educational Testing Service.PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_1_overlay_some",{id:"formSmash:items:resultList:47:j_idt627:1:overlay:some",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_1_overlay_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_1_overlay_otherAuthors",{id:"formSmash:items:resultList:47:j_idt627:1:overlay:otherAuthors",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_1_overlay_otherAuthors",multiple:true}); 2014 (English)In: Journal of educational measurement, ISSN 0022-0655, E-ISSN 1745-3984, Vol. 51, no 3, p. 223-238Article in journal (Refereed) Published##### Abstract [en]

We investigate the current bandwidth selection methods in kernel equating and propose a method based on Silverman's rule of thumb for selecting the bandwidth parameters. In kernel equating, the bandwidth parameters have previously been obtained by minimizing a penalty function. This minimization process has been criticized by practitioners for being too complex and that it does not offer sufficient smoothing in certain cases. In addition, the bandwidth parameters have been treated as constants in the derivation of the standard error of equating even when they were selected by considering the observed data. Here, the bandwidth selection is simplified, and modified standard errors of equating (SEEs) that reflect the bandwidth selection method are derived. The method is illustrated with real data examples and simulated data.

##### Place, publisher, year, edition, pages

Blackwell Publishing, 2014##### Keywords

kernel equating, observed-score test equating##### National Category

Probability Theory and Statistics##### Research subject

Statistics##### Identifiers

urn:nbn:se:uu:diva-223988 (URN)10.1111/jedm.12044 (DOI)000341592200001 ()PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_1_overlay_j_idt802",{id:"formSmash:items:resultList:47:j_idt627:1:overlay:j_idt802",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_1_overlay_j_idt802",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_1_overlay_j_idt808",{id:"formSmash:items:resultList:47:j_idt627:1:overlay:j_idt808",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_1_overlay_j_idt808",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_1_overlay_j_idt814",{id:"formSmash:items:resultList:47:j_idt627:1:overlay:j_idt814",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_1_overlay_j_idt814",multiple:true}); $(function(){PrimeFaces.cw("OverlayPanel","overlay714694",{id:"formSmash:items:resultList:47:j_idt627:1:j_idt631",widgetVar:"overlay714694",target:"formSmash:items:resultList:47:j_idt627:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});}); 3. Item Response Theory Observed-Score Kernel EquatingOpen this publication in new window or tab >>Item Response Theory Observed-Score Kernel Equating### Andersson, Björn

Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics. Beijing Normal Univ, Beijing, Peoples R China..### Wiberg, Marie

Department of Statistics, USBE, Umeå University, Sweden..PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_2_overlay_some",{id:"formSmash:items:resultList:47:j_idt627:2:overlay:some",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_2_overlay_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_2_overlay_otherAuthors",{id:"formSmash:items:resultList:47:j_idt627:2:overlay:otherAuthors",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_2_overlay_otherAuthors",multiple:true}); 2017 (English)In: Psychometrika, ISSN 0033-3123, E-ISSN 1860-0980, Vol. 82, no 1, p. 46-66Article in journal (Refereed) Published##### Abstract [en]

Item response theory (IRT) observed-score kernel equating is introduced for the non-equivalent groups with anchor test equating design using either chain equating or post-stratification equating. The equating function is treated in a multivariate setting and the asymptotic covariance matrices of IRT observed-score kernel equating functions are derived. Equating is conducted using the two-parameter and three-parameter logistic models with simulated data and data from a standardized achievement test. The results show that IRT observed-score kernel equating offers small standard errors and low equating bias under most settings considered.

##### Keywords

observed-score equating, item response theory, equipercentile equating, standard errors, NEAT design##### National Category

Probability Theory and Statistics##### Identifiers

urn:nbn:se:uu:diva-233484 (URN)10.1007/s11336-016-9528-7 (DOI)000394985400003 ()27743280 (PubMedID)PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_2_overlay_j_idt802",{id:"formSmash:items:resultList:47:j_idt627:2:overlay:j_idt802",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_2_overlay_j_idt802",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_2_overlay_j_idt808",{id:"formSmash:items:resultList:47:j_idt627:2:overlay:j_idt808",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_2_overlay_j_idt808",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_2_overlay_j_idt814",{id:"formSmash:items:resultList:47:j_idt627:2:overlay:j_idt814",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_2_overlay_j_idt814",multiple:true}); ##### Funder

Swedish Research Council, 2014-578 Available from: 2014-10-21 Created: 2014-10-06 Last updated: 2017-04-20Bibliographically approved$(function(){PrimeFaces.cw("OverlayPanel","overlay757287",{id:"formSmash:items:resultList:47:j_idt627:2:j_idt631",widgetVar:"overlay757287",target:"formSmash:items:resultList:47:j_idt627:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});}); 4. An Evaluation of Hypothesis Testing Methods for Equating Differences in Kernel EquatingOpen this publication in new window or tab >>An Evaluation of Hypothesis Testing Methods for Equating Differences in Kernel Equating### Andersson, Björn

PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_3_overlay_some",{id:"formSmash:items:resultList:47:j_idt627:3:overlay:some",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_3_overlay_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_3_overlay_otherAuthors",{id:"formSmash:items:resultList:47:j_idt627:3:overlay:otherAuthors",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_3_overlay_otherAuthors",multiple:true}); (English)Manuscript (preprint) (Other academic)##### Abstract [en]

In observed-score equating, hypothesis tests of equating differences are helpful in deciding which equating function is suitable. Here, a hypothesis testing procedure for item response theory (IRT) observed-score kernel equating using a Wald test is introduced. Simulations evaluating the Wald test when using IRT and log-linear models are conducted. The test with either IRT or log-linear models is shown to have high power and greatly outperform the Hommel multiple hypothesis testing method. The Wald test is applied to two datasets in both an equivalent groups design and a non-equivalent groups design, showing that the Wald test can provide different conclusions to other hypothesis testing methods in practice.

##### National Category

Probability Theory and Statistics##### Identifiers

urn:nbn:se:uu:diva-233486 (URN)PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_3_overlay_j_idt802",{id:"formSmash:items:resultList:47:j_idt627:3:overlay:j_idt802",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_3_overlay_j_idt802",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_3_overlay_j_idt808",{id:"formSmash:items:resultList:47:j_idt627:3:overlay:j_idt808",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_3_overlay_j_idt808",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_items_resultList_47_j_idt627_3_overlay_j_idt814",{id:"formSmash:items:resultList:47:j_idt627:3:overlay:j_idt814",widgetVar:"widget_formSmash_items_resultList_47_j_idt627_3_overlay_j_idt814",multiple:true}); $(function(){PrimeFaces.cw("OverlayPanel","overlay757290",{id:"formSmash:items:resultList:47:j_idt627:3:j_idt631",widgetVar:"overlay757290",target:"formSmash:items:resultList:47:j_idt627:3:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});}); PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:47:partsPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 49. Andersson, Björn PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_48_j_idt584",{id:"formSmash:items:resultList:48:j_idt584",widgetVar:"widget_formSmash_items_resultList_48_j_idt584",onLabel:"Andersson, Björn ",offLabel:"Andersson, Björn ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_48_j_idt587",{id:"formSmash:items:resultList:48:j_idt587",widgetVar:"widget_formSmash_items_resultList_48_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:48:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Bränberg, KennyDepartment of Statistics, USBE, Umeå University.Wiberg, MarieDepartment of Statistics, USBE, Umeå University.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:48:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Performing the Kernel Method of Test Equating with the Package kequate2013In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 55, no 6, p. 1-25Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_48_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:48:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_48_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); In standardized testing it is important to equate tests in order to ensure that the test takers, regardless of the test version given, obtain a fair test. Recently, the kernel method of test equating, which is a conjoint framework of test equating, has gained popularity. The kernel method of test equating includes five steps: (1) pre-smoothing, (2) estimation of the score probabilities, (3) continuization, (4) equating, and (5) computing the standard error of equating and the standard error of equating difference. Here, an implementation has been made for six different equating designs: equivalent groups, single group, counterbalanced, non-equivalent groups with anchor test using either chain equating or post-stratification equating, and non-equivalent groups using covariates. An R package for the kernel method of test equating called kequate is presented. Included in the package are also diagnostic tools aiding in the search for a proper log-linear model in the pre-smoothing step for use in conjunction with the R function glm.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:48:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 50. Andersson, Björn PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_49_j_idt584",{id:"formSmash:items:resultList:49:j_idt584",widgetVar:"widget_formSmash_items_resultList_49_j_idt584",onLabel:"Andersson, Björn ",offLabel:"Andersson, Björn ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_49_j_idt587",{id:"formSmash:items:resultList:49:j_idt587",widgetVar:"widget_formSmash_items_resultList_49_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:49:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); von Davier, Alina A.Educational Testing Service.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:49:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Book review of M. J. Kolen & R. L. Brennan (2014)*Test Equating, Scaling, and Linking: Methods and Practices Third Edition.*2015In: Psychometrika, ISSN 0033-3123, E-ISSN 1860-0980, Vol. 80, no 3, p. 856-858Article, book review (Other academic)

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