Bayesian Nonparametric Centered Random Effects Models with Variable Selection

• Activity Number: 26
• Type: Contributed
• Date/Time: Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Mingan Yang*+
• Companies: St. Louis University
• Keywords: Dirichlet process ; nonparametric Bayes ; Random effects ; Variable selection ; mixed effects model ; stochastic search

Abstract

In linear mixed effects model, it is common to assume the random effects to follow a parametric distribution such as normal distribution with mean zero. For variable selection in a linear mixed effects model, substantial violation of normality assumption can potentially impact the subset selection and result in poor interpretation and even incorrect results. For nonparametric random effects model, a challenge is to control the bias on the fixed effects by the random effects. In this article, we focus on a Bayesian method for variable selection. We characterize the subject specific random effects nonparametrically with Dirichlet process and resolve the bias simultaneously. The approach is implemented using a stochastic search Gibbs sampler to allow both fixed and random effects to be dropped effectively out of the model. Simulation and real data analysis are provided for illustration.