Bayesian Variable Selection for Skewed and Heteroscedastic Error

• Activity Number: 319
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Yuanyuan Tang and Debajyoti Sinha*+ and Yiyuan She and Stuart Lipsitz
• Companies: Florida State University and Florida State University and Florida State University and Brigham and Women's Hospital
• Keywords: Bayesian ; Variable Selection ; Skewed ; Heteroscedastic ; Outlier ; Heavy Tail

Abstract

The Bayesian approach to variable selection in regression is a powerful tool for dealing with many scientific problems. In this paper, we propose two novel models for Bayesian Variable Selection via transform-both-sides median regression functions. These two models provide the Bayesian Lasso related variable selection as well as robust estimations of parameters.