Document Detail


Bayesian nonparametric centered random effects models with variable selection.
MedLine Citation:
PMID:  23322356     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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.
Authors:
Mingan Yang
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-16
Journal Detail:
Title:  Biometrical journal. Biometrische Zeitschrift     Volume:  -     ISSN:  1521-4036     ISO Abbreviation:  Biom J     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-16     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7708048     Medline TA:  Biom J     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Affiliation:
Department of Mathematics, Central Michigan University, Mt. Pleasant, MI 48859, USA.
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