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A review of Bayesian group selection approaches for linear regression models
Author(s) -
Lai WeiTing,
Chen RayBing
Publication year - 2020
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1513
Subject(s) - bayesian probability , selection (genetic algorithm) , model selection , computer science , bayesian average , lasso (programming language) , group selection , bayesian linear regression , feature selection , bayesian inference , dimensionality reduction , variable order bayesian network , linear model , bayesian statistics , linear regression , machine learning , statistical model , graphical model , bayesian hierarchical modeling , artificial intelligence , world wide web
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