Objective Bayesian group variable selection for linear model

Sang Gil Kang (), Woo Dong Lee () and Yongku Kim ()
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Sang Gil Kang: Sangji University
Woo Dong Lee: Daegu Haany University
Yongku Kim: Kyungpook National University

Computational Statistics, 2022, vol. 37, issue 3, No 12, 1287-1310

Abstract: Abstract Prediction variables of the regression model are grouped in many application problems. For example, a factor in an analysis of variance can have several levels or each original prediction variable in additive models can be expanded into different order polynomials or a set of basis functions. It is essential to select important groups and individual variables within the selected groups. In this study, we propose the objective Bayesian group and individual variable selections within the selected groups in the regression model to reduce the computational cost, even though the number of regression variables is large. Besides, we examine the consistency of the proposed group variable selection procedure. The proposed objective Bayesian approach is investigated using simulation and real data examples. The comparisons between the penalized regression approaches, Bayesian group lasso and the proposed method are presented.

Keywords: Bayes factor; Group variable selection; Intrinsic prior; Linear regression model (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00180-021-01160-w

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