A0628
Title: Bayesian jackknife empirical likelihood
Authors: Yichen Cheng - Georgia State University (United States) [presenting]
Abstract: Empirical likelihood is a very powerful nonparametric tool that does not require any distributional assumptions. It has been previously shown that, if you replace the usual likelihood component in the Bayesian posterior likelihood with the empirical likelihood, then posterior inference is still valid when the functional of interest is a smooth function of the posterior mean. However, it is not clear whether similar conclusions can be obtained for parameters defined in terms of U-statistics. We propose the so-called Bayesian jackknife empirical likelihood, which replaces the likelihood component with the jackknife empirical likelihood. We show, both theoretically and empirically, the validity of the proposed method as a general tool for Bayesian inference. Empirical analysis shows the small sample performance of the proposed method is better than its frequentist counterpart. Analysis of a case-control study for pancreatic cancer is used to illustrate the new approach.
