TY - JOUR
T1 - Predictive bayesian model selection
AU - Ando, Tomohiro
N1 - Funding Information:
The author would like thank to helpful discussions and comments from seminar participants, University of Chicago and Northwestern University. The author is also grateful to Professor Arnold Zellner for helpful discussions. The author would like to thank the reviewer for constructive and helpful comments that have improved the quality of the paper. This research is partially supported by the Inamori Foundation, Japan.
PY - 2011
Y1 - 2011
N2 - We investigate the problem of evaluating the goodness of the predictive distributions of Bayesian models. Recently, deviance information criteria (DIC) has been extensively employed in various study areas to evaluate the Bayesian models, thanks to its simplicity of calculation from the posterior simulation outputs. Unfortunately, it is known that DIC often selects overfitted models. In this paper, we develop a new criterion which can be calculated easily from posterior outputs under the model misspecification situation. The proposed criterion is developed as an estimator of the posterior mean of the expected likelihood and is robust to improper priors. Monte Carlo simulations are conducted to investigate the properties of the proposed criteria.
AB - We investigate the problem of evaluating the goodness of the predictive distributions of Bayesian models. Recently, deviance information criteria (DIC) has been extensively employed in various study areas to evaluate the Bayesian models, thanks to its simplicity of calculation from the posterior simulation outputs. Unfortunately, it is known that DIC often selects overfitted models. In this paper, we develop a new criterion which can be calculated easily from posterior outputs under the model misspecification situation. The proposed criterion is developed as an estimator of the posterior mean of the expected likelihood and is robust to improper priors. Monte Carlo simulations are conducted to investigate the properties of the proposed criteria.
KW - Effective number of parameters
KW - Empirical Bayes Markov chain Monte Carlo
KW - Hierarchical Bayesian model
KW - Model misspecification
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U2 - 10.1080/01966324.2011.10737798
DO - 10.1080/01966324.2011.10737798
M3 - Article
AN - SCOPUS:84868296532
SN - 0196-6324
VL - 31
SP - 13
EP - 38
JO - American Journal of Mathematical and Management Sciences
JF - American Journal of Mathematical and Management Sciences
IS - 1-2
ER -