Deviance Information Criterion for Bayesian Model Selection: Justification and Variation
Yong Li (),
Jun Yu () and
Tao Zeng ()
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Yong Li: Hanqing Advanced Institute of Economics and Finance, Renmin University of China
Tao Zeng: Department of Finance, Wuhan University
No 5-2017, Economics and Statistics Working Papers from Singapore Management University, School of Economics
Deviance information criterion (DIC) has been extensively used for making Bayesian model selection. It is a Bayesian version of AIC and chooses a model that gives the smallest expected Kullback-Leibler divergence between the data generating process (DGP) and a predictive distribution asymptotically. We show that when the plug-in predictive distribution is used, DIC can have a rigorous decision-theoretic justification under regularity conditions. An alternative expression for DIC, based on the Bayesian predictive distribution, is proposed. The new DIC has a smaller penalty term than the original DIC and is very easy to compute from the MCMC output. It is invariant to reparameterization and yields a smaller frequentist risk than the original DIC asymptotically.
Keywords: AIC; DIC; Bayesian Predictive Distribution; Plug-in Predictive Distribution; Loss Function; Bayesian Model Comparison; Frequentist Risk (search for similar items in EconPapers)
JEL-codes: C11 C12 G12 (search for similar items in EconPapers)
Pages: 40 pages
New Economics Papers: this item is included in nep-ecm
Note: Paper available on: http://ink.library.smu.edu.sg/soe_research/1927
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Persistent link: https://EconPapers.repec.org/RePEc:ris:smuesw:2017_005
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