Affordable Access

deepdyve-link deepdyve-link
Publisher Website

Bayesian nonparametric centered random effects models with variable selection.

Authors
  • Yang, Mingan
Type
Published Article
Journal
Biometrical journal. Biometrische Zeitschrift
Publication Date
Mar 01, 2013
Volume
55
Issue
2
Pages
217–230
Identifiers
DOI: 10.1002/bimj.201100149
PMID: 23322356
Source
Medline
License
Unknown

Abstract

In a linear mixed effects model, it is common practice to assume that the random effects follow a parametric distribution such as a normal distribution with mean zero. However, in the case of variable selection, substantial violation of the normality assumption can potentially impact the subset selection and result in poor interpretation and even incorrect results. In nonparametric random effects models, the random effects generally have a nonzero mean, which causes an identifiability problem for the fixed effects that are paired with the random effects. In this article, we focus on a Bayesian method for variable selection. We characterize the subject-specific random effects nonparametrically with a Dirichlet process and resolve the bias simultaneously. In particular, we propose flexible modeling of the conditional distribution of the random effects with changes across the predictor space. The approach is implemented using a stochastic search Gibbs sampler to identify subsets of fixed effects and random effects to be included in the model. Simulations are provided to evaluate and compare the performance of our approach to the existing ones. We then apply the new approach to a real data example, cross-country and interlaboratory rodent uterotrophic bioassay.

Report this publication

Statistics

Seen <100 times