文章基本信息

标题：A Kullback-Leibler Divergence for Bayesian Model Diagnostics
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作者：Chen-Pin Wang ; Malay Ghosh
期刊名称：Open Journal of Statistics
印刷版ISSN：2161-718X
电子版ISSN：2161-7198
出版年度：2011
卷号：1
期号：3
页码：172-184
DOI：10.4236/ojs.2011.13021
出版社：Scientific Research Publishing
摘要：This paper considers a Kullback-Leibler distance (KLD) which is asymptotically equivalent to the KLD by Goutis and Robert [1] when the reference model (in comparison to a competing fitted model) is correctly specified and that certain regularity conditions hold true (ref. Akaike [2]). We derive the asymptotic property of this Goutis-Robert-Akaike KLD under certain regularity conditions. We also examine the impact of this asymptotic property when the regularity conditions are partially satisfied. Furthermore, the connection between the Goutis-Robert-Akaike KLD and a weighted posterior predictive p-value (WPPP) is established. Finally, both the Goutis-Robert-Akaike KLD and WPPP are applied to compare models using various simulated examples as well as two cohort studies of diabetes.
关键词：Kullback-Leibler Distance; Model Diagnostic; Weighted Posterior Predictive P-Value