Prof. Xuming He : Bayesian Empirical Likelihood for Quantile Regression

Posted by Min Yang , part of the Statistics and Data Science Seminar.

At

Nov. 14, 2012, 4 p.m.

In

SEO 636

Abstract

Quantile regression is semiparametric in the sense that no parametric likelihood is assumed in the model. A working likelihood can be used, but the resulting posterior may not have the validity for statistical inference. In this talk we will introduce Bayesian empirical likelihood for quantile regression, and show that it leads to asymptotically valid posterior inference. In addition, this approach enables us to make use of commonality across quantiles to improve efficiency of quantile estimation in data sparse areas. We will also introduce a notion of shrinking priors, and demonstrate how this new framework can help explain the efficiency gains of the Bayesian empirical likelihood method over the usual quantile estimates. The talk is based on joint work with Yunwen Yang (Drexel University).