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Activity Number: 184
Type: Contributed
Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #307723
Title: Empirical Likelihood-Based Deviance Information Criterion
Author(s): Teng Yin*+ and Sanjay Chaudhuri
Companies: and National Univ. of Singapore
Keywords: Bayesian empirical likelihood ; Deviance information criterion ; Effective number of parameters ; Model comparison

The Deviance information criterion (DIC) (Spiegelhalter el al, 2002) for model assessment and model comparison is constructed based on the posterior expectation of the deviance. In recent years, DIC has been applied on many statistical models. On a separate track recently, empirical likelihood based semi-parametric methods have been used in Bayesian framework. In this paper, we propose the empirical likelihood based deviance information criterion for Bayesian empirical likelihood. Moreover, similar to parametric models, a measure for effective number of param- eters is also derived. Simulation results show that our method can be applied on variable selection in linear and generalized linear models. In addition, we investigate the influence of different prior distribu- tions on posterior inference and on the measure for effective number of parameters.

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