A review of Bayesian group selection approaches for linear regression models

Wei Ting Lai, Ray Bing Chen

Research output: Contribution to journalReview article

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.

Original languageEnglish
JournalWiley Interdisciplinary Reviews: Computational Statistics
DOIs
Publication statusAccepted/In press - 2020

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

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