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  • 标题:A Kullback-Leibler Divergence for Bayesian Model Diagnostics
  • 本地全文:下载
  • 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
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