Bayesian Variable Selection in Linear Mixed Models with Shrinkage Priors

• Activity Number: 637
• Type: Topic Contributed
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: International Indian Statistical Association
• Author(s): Mingan Yang*+
• Companies: Central Michigan University
• Keywords: shrinkage ; mixed effects model ; variable selection ; model averaging ; Bayesian model selection

Abstract

In this article, we address the problem of joint selection of both fixed effects and random effects with the shrinkage priors in linear mixed models. The idea is to shrink small coefficients close to zero while minimally shrink large coefficients due to the heavy tails. The shrinkage priors can be obtained via a scale mixture of normal distributions to facilitate computation. We use a stochastic search Gibbs sampler to implement a fully Bayesian variable selection. The approach is illustrated using simulated data and real example.