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Publications (10 of 29) Show all publications
Lindholm, A., Zachariah, D., Stoica, P. & Schön, T. B. (2019). Data consistency approach to model validation. IEEE Access, 7, 59788-59796
Open this publication in new window or tab >>Data consistency approach to model validation
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 59788-59796Article in journal (Refereed) Published
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-386361 (URN)10.1109/ACCESS.2019.2915109 (DOI)000468615500001 ()
Available from: 2019-05-07 Created: 2019-06-24 Last updated: 2019-06-26Bibliographically approved
Zachariah, D. & Stoica, P. (2019). Effect Inference From Two-Group Data With Sampling Bias. IEEE Signal Processing Letters, 26(8), 1103-1106
Open this publication in new window or tab >>Effect Inference From Two-Group Data With Sampling Bias
2019 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 26, no 8, p. 1103-1106Article in journal (Refereed) Published
Abstract [en]

In many applications, different populations are compared using data that are sampled in a biased manner. Under sampling biases, standard methods that estimate the difference between the population means yield unreliable inferences. Here, we develop an inference method that is resilient to sampling biases and is able to control the false positive errors under moderate bias levels in contrast to the standard approach. We demonstrate the method using synthetic and real biomarker data.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019
Keywords
Sampling bias, statistical inference, two-sample test
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-390076 (URN)10.1109/LSP.2019.2921255 (DOI)000472209500002 ()
Funder
Swedish Research Council, 2018-05040
Available from: 2019-08-06 Created: 2019-08-06 Last updated: 2019-08-06Bibliographically approved
Venkitaraman, A. & Zachariah, D. (2019). Learning sparse graphs for prediction of multivariate data processes. IEEE Signal Processing Letters, 26(3), 495-499
Open this publication in new window or tab >>Learning sparse graphs for prediction of multivariate data processes
2019 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 26, no 3, p. 495-499Article in journal (Refereed) Published
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-379032 (URN)10.1109/LSP.2019.2896435 (DOI)000458852100005 ()
Available from: 2019-01-30 Created: 2019-03-11 Last updated: 2019-04-06Bibliographically approved
Das, A., Zachariah, D. & Stoica, P. (2018). Comparison of two hyperparameter-free sparse signal processing methods for direction-of-arrival tracking in the HF97 ocean acoustic experiment. IEEE Journal of Oceanic Engineering, 43(3), 725-734
Open this publication in new window or tab >>Comparison of two hyperparameter-free sparse signal processing methods for direction-of-arrival tracking in the HF97 ocean acoustic experiment
2018 (English)In: IEEE Journal of Oceanic Engineering, ISSN 0364-9059, E-ISSN 1558-1691, Vol. 43, no 3, p. 725-734Article in journal (Refereed) Published
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-335107 (URN)10.1109/JOE.2017.2706100 (DOI)000438742000013 ()
Available from: 2017-06-06 Created: 2017-11-30 Last updated: 2018-11-24Bibliographically approved
Svensson, A., Zachariah, D. & Schön, T. B. (2018). How consistent is my model with the data?: Information-theoretic model check. In: : . Paper presented at SYSID 2018, July 9–11, Stockholm, Sweden (pp. 407-412).
Open this publication in new window or tab >>How consistent is my model with the data?: Information-theoretic model check
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The choice of model class is fundamental in statistical learning and system identification, no matter whether the class is derived from physical principles or is a generic black-box. We develop a method to evaluate the specified model class by assessing its capability of reproducing data that is similar to the observed data record. This model check is based on the information-theoretic properties of models viewed as data generators and is applicable to e.g. sequential data and nonlinear dynamical models. The method can be understood as a specific two-sided posterior predictive test. We apply the information-theoretic model check to both synthetic and real data and compare it with a classical whiteness test.

Series
IFAC-PapersOnLine, ISSN 2405-8963 ; 51:15
National Category
Probability Theory and Statistics Control Engineering
Identifiers
urn:nbn:se:uu:diva-368622 (URN)10.1016/j.ifacol.2018.09.179 (DOI)000446599200070 ()
Conference
SYSID 2018, July 9–11, Stockholm, Sweden
Funder
Swedish Research Council, 2016-06079Swedish Research Council, 621-2014-5874Swedish Foundation for Strategic Research , RIT15-0012
Available from: 2018-10-08 Created: 2018-12-05 Last updated: 2018-12-14Bibliographically approved
Mattsson, P., Zachariah, D. & Stoica, P. (2018). Identification of cascade water tanks using a PWARX model. Mechanical systems and signal processing, 106, 40-48
Open this publication in new window or tab >>Identification of cascade water tanks using a PWARX model
2018 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 106, p. 40-48Article in journal (Refereed) Published
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-350267 (URN)10.1016/j.ymssp.2017.12.027 (DOI)000426229100004 ()
Available from: 2018-01-05 Created: 2018-05-14 Last updated: 2018-11-24Bibliographically approved
Olsson, F., Halvorsen, K., Zachariah, D. & Mattsson, P. (2018). Identification of nonlinear feedback mechanisms operating in closed loop using inertial sensors. In: : . Paper presented at 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, 9–11 July, 2018 (pp. 473-478). IFAC Papers Online, 51
Open this publication in new window or tab >>Identification of nonlinear feedback mechanisms operating in closed loop using inertial sensors
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we study the problem of identifying linear and nonlinear feedback mechanisms, or controllers, operating in closed loop. A recently developed identification method for nonlinear systems, the LAVA method, is used for this purpose. Identification data is obtained from inertial sensors, that provide information about the movement of the system, in the form of linear acceleration and angular velocity measurements. This information is different from the information that is available to the controller to be identified, which makes use of unknown internal sensors instead. We provide two examples, a simulated neuromuscular controller in standing human balance, and a lead-filter controlling a physical position servo using a DC motor. Both linear and nonlinear controllers are used in the examples. We show that the LAVA method is able to identify sparse, parsimonious models of the controllers.

Place, publisher, year, edition, pages
IFAC Papers Online, 2018
Series
IFAC-PapersOnLine, ISSN 2405-8963 ; 51:15
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-350607 (URN)10.1016/j.ifacol.2018.09.190 (DOI)000446599200081 ()
Conference
18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden, 9–11 July, 2018
Funder
Swedish Research Council, 2015-05054
Available from: 2018-10-08 Created: 2018-05-14 Last updated: 2018-12-11Bibliographically approved
Osama, M., Zachariah, D. & Schön, T. B. (2018). Learning localized spatio-temporal models from streaming data. In: Proc. 35th International Conference on Machine Learning: . Paper presented at ICML 2018, July 10–15, Stockholm, Sweden (pp. 3927-3935).
Open this publication in new window or tab >>Learning localized spatio-temporal models from streaming data
2018 (English)In: Proc. 35th International Conference on Machine Learning, 2018, p. 3927-3935Conference paper, Published paper (Refereed)
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-368621 (URN)
Conference
ICML 2018, July 10–15, Stockholm, Sweden
Funder
Swedish Foundation for Strategic Research , RIT15-0012Swedish Research Council, 621-2016-06079
Available from: 2018-12-05 Created: 2018-12-05 Last updated: 2019-04-06Bibliographically approved
Zachariah, D. & Stoica, P. (2018). Model-robust counterfactual prediction method. In: ICML Workshop on Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action: . Paper presented at CausalML 2018, July 15, Stockholm, Sweden.
Open this publication in new window or tab >>Model-robust counterfactual prediction method
2018 (English)In: ICML Workshop on Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action, 2018Conference paper, Poster (with or without abstract) (Refereed)
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:uu:diva-366774 (URN)
Conference
CausalML 2018, July 15, Stockholm, Sweden
Available from: 2018-07-15 Created: 2018-11-24 Last updated: 2018-11-24Bibliographically approved
Mattsson, P., Zachariah, D. & Stoica, P. (2018). Recursive nonlinear-system identification using latent variables. Automatica, 93, 343-351
Open this publication in new window or tab >>Recursive nonlinear-system identification using latent variables
2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 93, p. 343-351Article in journal (Refereed) Published
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-360432 (URN)10.1016/j.automatica.2018.03.007 (DOI)000436916200036 ()
Available from: 2018-04-01 Created: 2018-09-17 Last updated: 2018-11-24Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6698-0166

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