A novel group VIF regression for group variable selection with application to multiple change-point detection

Hao Ding, Yan Zhang and Yuehua Wu

Journal of Applied Statistics, 2023, vol. 50, issue 2, 247-263

Abstract: In this paper, we propose a novel group variance inflation factor (VIF) regression model for tackling large data sets where data follows a grouped structure. Unlike classical penalized methods, this approach can perform group variable selection in a sparse model, which is quite different from the classical penalized methods. We further adapt the proposed method associated with a two-stage procedure for detecting multiple change-point in linear models. We carry out extensive simulation studies to show that the proposed group variable selection and change-point detection methods are stable and efficient. Finally, we provide two real data examples, including a body fat data set and an air pollution data set, to illustrate the performance of our algorithms in group selection and change-point detection.

Date: 2023
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DOI: 10.1080/02664763.2021.1987400

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