Online Program Home
My Program

Abstract Details

Activity Number: 641 - Recent Advances in Density Mixture Modeling and EM-Like Algorithms: Frequentist and Bayesian Views
Type: Topic Contributed
Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #305254 Presentation
Title: Prediction Risk in Linear Regression Models Under Global-Local Mixture Priors
Author(s): Anindya Bhadra* and Jyotishka Datta and Yunfan Li and Nicholas Polson and Brandon Willard
Companies: Purdue University and University of Arkansas and Purdue University and University of Chicago and University of Chicago
Keywords: horseshoe; shrinkage regression; Stein's unbiased risk estimate; principal components; ridge regression; horseshoe+

Predictive performance in shrinkage regression suffers from two major difficulties: (i) the amount of relative shrinkage is monotone in the singular values of the design matrix and (ii) the amount of shrinkage does not depend on the response variables. Both of these factors can translate to a poor prediction performance, the risk of which can be estimated unbiasedly using Stein's approach. We show that using a component-specific local shrinkage term that can be learned from the data under a suitable heavy-tailed prior, in combination with a global term providing shrinkage towards zero, can alleviate both these difficulties and consequently, can result in an improved risk for prediction. Demonstrations of improved prediction performance over competing approaches in a simulation study and in a pharmacogenomics data set confirm our theoretical findings.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program