Bayesian adaptive lasso with variational Bayes for variable selection in high-dimensional generalized linear mixed models. Issue 2 (7th February 2019)

• Record Type: Journal Article
• Authors: Tung, Dao Thanh; Tran, Minh-Ngoc; Cuong, Tran Manh
• Is Part Of: Communications in statistics. Volume 48:Issue 2(2019)
• Page Start: 530
• Page End: 543
• Publication Date: 2019-02-07
• Subjects: EM algorithm -- High dimensions -- Lasso -- Posterior mode; 62J07 -- 62J12; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2017.1387663
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: This article describes a full Bayesian treatment for simultaneous fixed-effect selection and parameter estimation in high-dimensional generalized linear mixed models. The approach consists of using a Bayesian adaptive Lasso penalty for signal-level adaptive shrinkage and a fast Variational Bayes scheme for estimating the posterior mode of the coefficients. The proposed approach offers several advantages over the existing methods, for example, the adaptive shrinkage parameters are automatically incorporated, no Laplace approximation step is required to integrate out the random effects. The performance of our approach is illustrated on several simulated and real data examples. The algorithm is implemented in theR packageglmmvb and is made available online.