Online Program Home
My Program

Abstract Details

Activity Number: 665
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #321262
Title: Generalized Orthant Normal and L1-Regularized G Priors
Author(s): Christopher Hans*
Companies: The Ohio State University
Keywords: Bayesian lasso ; g prior ; Bayesian elastic net ; regularization

Many regularization priors for Bayesian regression assume the regression coefficients are a priori independent. In particular this is the case for Bayesian treatments of the lasso and the elastic net. While independence may be reasonable in some data-analytic settings, having the ability to incorporate dependence in these prior distributions would allow for greater modeling flexibility. This paper introduces the orthant normal distribution in a general form and shows how it can be used to structure prior dependence in Bayesian regression models that have connections to penalized optimization procedures. An L1-regularized version of Zellner's g prior is introduced as a special case, creating a new link between the literature on penalized optimization and an important class of regression priors.

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

Back to the full JSM 2016 program

Copyright © American Statistical Association