AN EMPIRICAL BAYES INFORMATION CRITERION FOR SELECTING VARIABLES IN LINEAR MIXED MODELS
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概要
- 論文の詳細を見る
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.
- 日本統計学会の論文
- 2010-06-01
著者
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Srivastava Muni
Department of Statistics, University of Toronto
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Srivastava Muni
Department Of Statistics University Of Toronto
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KUBOKAWA Tatsuya
Faculty of Economics, University of Tokyo
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Kubokawa Tatsuya
Faculty Of Economics University Of Tokyo
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