Publication Type
Working Paper
Version
publishedVersion
Publication Date
2-2017
Abstract
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 expected loss than the original DIC asymptotically.
Keywords
AIC, DIC, Bayesian Predictive Distribution, Plug-in Predictive Distribution, Loss Function, Bayesian Model Comparison, Frequentist Risk
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
40
Publisher
SMU Economics and Statistics Working Paper, No. 15-2017
City or Country
Singapore
Citation
LI, Yong; Jun YU; and ZENG, Tao.
Deviance information criterion for Bayesian model selection: Justification and variation. (2017). 1-40.
Available at: https://ink.library.smu.edu.sg/soe_research/1927
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Comments
Newer version at http://www.mysmu.edu/faculty/yujun/Research/DIC_Theory27.pdf