Qing Li
 

Department of Mathematics, Washington University
 

Title: Bayesian Regularized Quantile Regression
 

Abstract: Regularization has been shown to be effective in quantile regression in improving the prediction accuracy. This talk studies regularization in quantile regressions from a Bayesian perspective. By proposing a hierarchical model framework, we give a generic treatment to a set of regularization approaches, including lasso, group lasso and elastic net penalties. Gibbs samplers are derived for all cases. Both simulated and real data examples show that Bayesian regularized quantile regression methods often outperform quantile regression without regularization and their non-Bayesian counterparts with regularization.