Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Comp Stat

A review of Bayesian group selection approaches for linear regression models

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Abstract Grouping selection arises naturally in many statistical modeling problems. Several group selection methods have been proposed in the last two decades. In this paper, we review the Bayesian group selection approaches for linear regression models. We start from the Bayesian indicator approach and then move to the Bayesian group LASSO methods. In addition, we also consider the Bayesian methods for the sparse group selection that can be treated as an extension of the group selection. Finally, we mention some extensions of Bayesian group selection for the generalized linear models and the multiple response models. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Dimension Reduction Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Statistical Models > Model Selection

Browse by Topic

Statistical Models > Model Selection
Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory
Statistical and Graphical Methods of Data Analysis > Dimensional Reduction

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts