Title:Hypothesis Testing Using Posterior-test-based Bayes Factor
Time:2022.5.12 19:30-21:00
Presenter: Prof. Li Yong from Renmin University
Host:Prof. Hong Jieying
Abstract:
Hypothesis testing based on p-values has been criticized in recent years. The conventional Bayes factors (BFs) have been tipped as possible replacements of p-values. However, conventional BFs suffer from several theoretical and practical difficulties. For example, the conventional BFs are not well-defined under improper priors and they subject to Jeffreys-Lindley-Bartlett's paradox when proper but vague priors are used. Moreover, they are difficult to compute for many models. In this paper, we propose to compare the sampling distributions of the posterior-test-based statistics for hypothesis testing. Two posterior-test-based statistics are considered, namely the posterior version of likelihood ratio (LR) test and the posterior version of Wald test. Under some regularity conditions, we establish the consistency property of the new method. We also show how the proposed method can address the problems in p-values and those in the conventional BFs. The advantages of the proposed method are highlighted using several simulation studies and empirical studies.
