Covariance Selection in the Linear Mixed Effect Mode

Jonathan P. Williams, Ying Lu
Proceedings of the 1st International Workshop on Feature Extraction: Modern Questions and Challenges at NIPS 2015, PMLR 44:277-291, 2015.

Abstract

This paper improves and extends the two-step penalized iterative estimation procedure for the linear mixed effect model (LMM) by explicitly penalizing the off-diagonal components of the covariance matrix of random effects. To explicitly penalize the off-diagonal terms in the covariance matrix of random effects, glasso is incorporated in the penalized LMM approach. The paper also provides theoretical justification and a computational algorithm for the provided approach. Empirical analysis using random simulated data shows that explicitly penalizing the off-diagonal covariance components can greatly improve the model selection procedure.

Cite this Paper

BibTeX
@InProceedings{pmlr-v44-Williams2015, title = {Covariance Selection in the Linear Mixed Effect Mode}, author = {Williams, Jonathan P. and Lu, Ying}, booktitle = {Proceedings of the 1st International Workshop on Feature Extraction: Modern Questions and Challenges at NIPS 2015}, pages = {277--291}, year = {2015}, editor = {Storcheus, Dmitry and Rostamizadeh, Afshin and Kumar, Sanjiv}, volume = {44}, series = {Proceedings of Machine Learning Research}, address = {Montreal, Canada}, month = {11 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v44/Williams2015.pdf}, url = {https://proceedings.mlr.press/v44/Williams2015.html}, abstract = {This paper improves and extends the two-step penalized iterative estimation procedure for the linear mixed effect model (LMM) by explicitly penalizing the off-diagonal components of the covariance matrix of random effects. To explicitly penalize the off-diagonal terms in the covariance matrix of random effects, glasso is incorporated in the penalized LMM approach. The paper also provides theoretical justification and a computational algorithm for the provided approach. Empirical analysis using random simulated data shows that explicitly penalizing the off-diagonal covariance components can greatly improve the model selection procedure.} }
Endnote
%0 Conference Paper %T Covariance Selection in the Linear Mixed Effect Mode %A Jonathan P. Williams %A Ying Lu %B Proceedings of the 1st International Workshop on Feature Extraction: Modern Questions and Challenges at NIPS 2015 %C Proceedings of Machine Learning Research %D 2015 %E Dmitry Storcheus %E Afshin Rostamizadeh %E Sanjiv Kumar %F pmlr-v44-Williams2015 %I PMLR %P 277--291 %U https://proceedings.mlr.press/v44/Williams2015.html %V 44 %X This paper improves and extends the two-step penalized iterative estimation procedure for the linear mixed effect model (LMM) by explicitly penalizing the off-diagonal components of the covariance matrix of random effects. To explicitly penalize the off-diagonal terms in the covariance matrix of random effects, glasso is incorporated in the penalized LMM approach. The paper also provides theoretical justification and a computational algorithm for the provided approach. Empirical analysis using random simulated data shows that explicitly penalizing the off-diagonal covariance components can greatly improve the model selection procedure.
RIS
TY - CPAPER TI - Covariance Selection in the Linear Mixed Effect Mode AU - Jonathan P. Williams AU - Ying Lu BT - Proceedings of the 1st International Workshop on Feature Extraction: Modern Questions and Challenges at NIPS 2015 DA - 2015/12/08 ED - Dmitry Storcheus ED - Afshin Rostamizadeh ED - Sanjiv Kumar ID - pmlr-v44-Williams2015 PB - PMLR DP - Proceedings of Machine Learning Research VL - 44 SP - 277 EP - 291 L1 - http://proceedings.mlr.press/v44/Williams2015.pdf UR - https://proceedings.mlr.press/v44/Williams2015.html AB - This paper improves and extends the two-step penalized iterative estimation procedure for the linear mixed effect model (LMM) by explicitly penalizing the off-diagonal components of the covariance matrix of random effects. To explicitly penalize the off-diagonal terms in the covariance matrix of random effects, glasso is incorporated in the penalized LMM approach. The paper also provides theoretical justification and a computational algorithm for the provided approach. Empirical analysis using random simulated data shows that explicitly penalizing the off-diagonal covariance components can greatly improve the model selection procedure. ER -
APA
Williams, J.P. & Lu, Y.. (2015). Covariance Selection in the Linear Mixed Effect Mode. Proceedings of the 1st International Workshop on Feature Extraction: Modern Questions and Challenges at NIPS 2015, in Proceedings of Machine Learning Research 44:277-291 Available from https://proceedings.mlr.press/v44/Williams2015.html.

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