Bayesian model diagnostics using functional Bregman divergence

JOURNAL OF MULTIVARIATE ANALYSIS

卷:124

Article

Goh, Gyuhyeong¹  Dey, Dipak K.¹ 

[1] Univ Connecticut, Dept Stat, Storrs, CT 06269 USA

关键词: Bayesian robustness;    Bregman divergence;    Circular data;    Gaussian approximation;    Importance sampling;    Markov chain Monte Carlo;   

DOI  :  10.1016/j.jmva.2013.11.008

来源: Elsevier

【 摘 要 】

It is crucial to check validation of any statistical model after fitting it for a given set of data. In Bayesian statistics, a researcher can check the fit of the model using a variety of strategies. In this paper we consider two major aspects, first checking that the posterior inferences are reasonable, given the substantive context of the model; and then examining the sensitivity of inferences to reasonable changes in the prior distribution and the likelihood. Here we consider functional Bregman divergence between posterior distributions for model diagnostics, which produce methods for outlier detection as well as for prior sensitivity analysis. The methodology is exemplified through a logistic regression and a circular data model. (C) 2013 Elsevier Inc. All rights reserved.

【 授权许可】

Free