Bayesian adaptive lasso with variational Bayes for variable selection in high-dimensional generalized linear mixed models. Issue 2 (7th February 2019) - Tung, Dao Thanh; Tran, Minh-Ngoc; Cuong, Tran Manh
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.