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  • 1.
    Ahmad, M. Rauf
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
    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]

    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.

  • 2.
    Ahmad, M. Rauf
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
    von Rosen, D.
    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]

    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.

  • 3.
    Andersson, Björn
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
    Waernbaum, Ingeborg
    Sensitivity analysis of violations of the faithfulness assumption2014In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 84, no 7, p. 1608-1620Article in journal (Refereed)
    Abstract [en]

    We study the implication of violations of the faithfulness condition due to parameter cancellations on estimation of the directed acyclic graph (DAG) skeleton. Three settings are investigated: when (i) faithfulness is guaranteed (ii) faithfulness is not guaranteed and (iii) the parameter distributions are concentrated around unfaithfulness (near-unfaithfulness). In a simulation study, the effects of the different settings are compared using the parents and children (PC) and max–min parents and children (MMPC) algorithms. The results show that the performance in the faithful case is almost unchanged compared with the unrestricted case, whereas there is a general decrease in performance under the near-unfaithful case as compared with the unrestricted case. The response to near-unfaithful parameterizations is similar between the two algorithms, with the MMPC algorithm having higher true positive rates and the PC algorithm having lower false positive rates.

  • 4.
    Cao, Chunzheng
    et al.
    Nanjing University of Information Science and Technology.
    Chen, Mengqian
    Nanjing University of Information Science and Technology.
    Zhu, Xiaoxin
    Nanjing University of Information Science and Technology.
    Jin, Shaobo
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
    Bayesian inference in a heteroscedastic replicated measurement error model using heavy-tailed distributions2017In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 87, no 15, p. 2915-2928Article in journal (Refereed)
    Abstract [en]

    We introduce a multivariate heteroscedastic measurement error model for replications under scale mixtures of normal distribution. The model can provide a robust analysis and can be viewed as a generalization of multiple linear regression from both model structure and distribution assumption. An efficient method based on Markov Chain Monte Carlo is developed for parameter estimation. The deviance information criterion and the conditional predictive ordinates are used as model selection criteria. Simulation studies show robust inference behaviours of the model against both misspecification of distributions and outliers. We work out an illustrative example with a real data set on measurements of plant root decomposition.

  • 5.
    Eklund, Martin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Zwanzig, Silvelyn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics.
    SimSel: a new simulation method for variable selection2012In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 82, no 4, p. 515-527Article in journal (Refereed)
    Abstract [en]

    We propose a new simulation method, SimSel, for variable selection in linear and nonlinear modelling problems. SimSel works by disturbing the input data with pseudo-errors. We then study how this disturbance affects the quality of an approximative model fitted to the data. The main idea is that disturbing unimportant variables does not affect the quality of the model fit. The use of an approximative model has the advantage that the true underlying function does not need to be known and that the method becomes insensitive to model misspecifications. We demonstrate SimSel on simulated data from linear and nonlinear models and on two real data sets. The simulation studies suggest that SimSel works well in complicated situations, such as nonlinear errors-in-variable models.

  • 6. Johansson, Per
    Tests for serial correlation and overdispersion in a count data regression model1995In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 53, p. 153-164Article in journal (Refereed)
  • 7.
    Karlsson, Andreas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Centre for Clinical Research, County of Västmanland.
    Bootstrap Methods for Bias Correction and Confidence Interval Estimation for Nonlinear Quantile Regression of Longitudinal Data2009In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 79, no 10, p. 1205-1218Article in journal (Refereed)
    Abstract [en]

    This paper examines the use of bootstrapping for bias correction and   calculation of confidence intervals (CIs) for a weighted nonlinear   quantile regression estimator adjusted to the case of longitudinal   data. Different weights and types of CIs are used and compared by   computer simulation using a logistic growth function and error terms   following an AR(1) model. The results indicate that bias correction   reduces the bias of a point estimator but fails for CI calculations. A   bootstrap percentile method and a normal approximation method perform   well for two weights when used without bias correction. Taking both   coverage and lengths of CIs into consideration, a non-bias-corrected   percentile method with an unweighted estimator performs best.

  • 8.
    Liu, Xijia
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
    Preve, Daniel
    City University of Hong Kong.
    Measure of location-based estimators in simple linear regression2016In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 86, no 9, p. 1771-1784Article in journal (Refereed)
    Abstract [en]

    In this note we consider certain measure of location-based estimators (MLBEs) for the slope parameter in a linear regression model with a single stochastic regressor. The median-unbiased MLBEs are interesting as they can be robust to heavy-tailed samples and, hence, preferable to the ordinary least squares estimator (LSE). Two different cases are considered as we investigate the statistical properties of the MLBEs. In the first case, the regressor and error is assumed to follow a symmetric stable distribution. In the second, other types of regressions, with potentially contaminated errors, are considered. For both cases the consistency and exact finite-sample distributions of the MLBEs are established. Some results for the corresponding limiting distributions are also provided. In addition, we illustrate how our results can be extended to include certain heteroskedastic and multiple regressions. Finite-sample properties of the MLBEs in comparison to the LSE are investigated in a simulation study.

  • 9.
    Lyhagen, Johan
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
    Kraus, Katrin
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
    The small sample performance of estimators of the standard errors of structural equation models2013In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 83, no 3, p. 458-471Article in journal (Refereed)
    Abstract [en]

    n this paper, we compare five asymptotically, under a correctly specified likelihood, equivalent estimators of the standard errors for parameters in structuralequation models. The estimators are evaluated under different conditions regarding (i)sample size, varying between N=50 and 3200, (ii) distributional assumption of the latent variables and the disturbance terms, namely normal, and heavy tailed (t), and (iii) thecomplexity of the model. For the assessment of the five estimators we use overallperformance, relative bias, MSE and coverage of confidence intervals. The analysis reveals substantial differences in the performance of the five asymptotically equalestimators. Most diversity was found for t distributed, i.e. heavy tailed, data.

  • 10.
    Lyhagen, Johan
    et al.
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
    Kraus, Katrin
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
    The small sample performance of estimators of the standard errors of structural equation models2013In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 83, no 3, p. 458-471Article in journal (Refereed)
    Abstract [en]

    n this paper, we compare five asymptotically, under a correctly specified likelihood, equivalent estimators of the standard errors for parameters in structuralequation models. The estimators are evaluated under different conditions regarding (i)sample size, varying between N=50 and 3200, (ii) distributional assumption of the latent variables and the disturbance terms, namely normal, and heavy tailed (t), and (iii) thecomplexity of the model. For the assessment of the five estimators we use overallperformance, relative bias, MSE and coverage of confidence intervals. The analysis reveals substantial differences in the performance of the five asymptotically equalestimators. Most diversity was found for t distributed, i.e. heavy tailed, data.

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