Sparse linear mixed model selection via streamlined variational Bayes-Reference-Cited by-同舟云学术

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Sparse linear mixed model selection via streamlined variational Bayes

Published:2022-01-01 Issue:2 Volume:16 Page:
ISSN:1935-7524
Container-title:Electronic Journal of Statistics
language:
Short-container-title:Electron. J. Statist.

Author:

Degani Emanuele¹,Maestrini Luca²,Toczydłowska Dorota³,Wand Matt P.³

Affiliation:

1. Dipartimento di Scienze Statistiche Università degli Studi di Padova, Padova, Italy

2. Research School of Finance, Actuarial Studies and Statistics The Australian National University, Canberra, Australia

3. School of Mathematical and Physical Sciences University of Technology Sydney, Sydney, Australia

Publisher

Institute of Mathematical Statistics

Subject

Statistics and Probability,Statistics, Probability and Uncertainty

Reference94 articles.

1. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York.

2. PARK, T. and CASELLA, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association 103 681–686.

3. ZOU, H. (2006). The Adaptive Lasso and Its Oracle Properties. Journal of the American Statistical Association 101 1418–1429.

4. ZOU, H. and HASTIE, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society. Series B (Statistical Methodology) 67 301–320.

5. ARMAGAN, A. and DUNSON, D. B. (2011). Sparse variational analysis of linear mixed models for large data sets. Statistics & Probability Letters 81 1056–1062.

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