JOURNAL OF THE JAPAN STATISTICAL SOCIETY
Online ISSN : 1348-6365
Print ISSN : 1882-2754
ISSN-L : 1348-6365
An Empirical Bayes Information Criterion for Selecting Variables in Linear Mixed Models
Tatsuya KubokawaMuni S. Srivastava
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JOURNAL FREE ACCESS

2010 Volume 40 Issue 1 Pages 111-131

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Abstract

The paper addresses the problem of selecting variables in linear mixed models (LMM)νll. We propose the Empirical Bayes Information Criterion (EBIC) using a partial prior information on the parameters of interest. Specifically EBIC incorporates a non-subjective prior distribution on regression coefficients with an unknown hyper-parameter, but it is free from the setup of a prior information on the nuisance parameters like variance components. It is shown that EBIC not only has the nice asymptotic property of consistency as a variable selection, but also performs better in small and large sample sizes than the conventional methods like AIC, conditional AIC and BIC in light of selecting true variables.

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© 2010 Japan Statistical Society
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