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Abstract Details
Activity Number:
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26
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #302928 |
Title:
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Bayesian Nonparametric Centered Random Effects Models with Variable Selection
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Author(s):
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Mingan Yang*+
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Companies:
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St. Louis University
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Address:
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Biostat Division, Saint Louis , MO, 63303,
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Keywords:
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Dirichlet process ;
nonparametric Bayes ;
Random effects ;
Variable selection ;
mixed effects model ;
stochastic search
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Abstract:
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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.
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Authors who are presenting talks have a * after their name.
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