A review of Bayesian group selection approaches for linear regression models. (29th June 2020)

• Record Type: Journal Article
• Authors: Lai, Wei‐Ting; Chen, Ray‐Bing
• Is Part Of: Wiley interdisciplinary reviews. Volume 13:Number 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-29
• Subjects: Bayesian indicator selection -- Bayesian LASSO -- sparse group; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1513
• Languages: English
• ISSNs: 1939-5108

Abstract

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 Abstract : Grouping selection arises naturally in many statistical modeling problems. Here 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 mention some extensions of Bayesian group selection for the generalized linear models and the multiple response models.