Journal article
Computation and analysis of change points with different jump locations in high-dimensional regression
Statistical papers (Berlin, Germany), Vol.65(3), pp.1703-1729
05/2024
DOI: 10.1007/s00362-023-01461-w
Abstract
The purpose of this paper is to study multiple structural changes that occur at unknown locations in high-dimensional linear regression. We consider a structural change model where the parameters are subject to shifts at possibly different locations. We propose a penalized least squares approach, combined with a temporal difference penalty term for the difference between the coefficients at successive points for identifying latent change points, as well as a common sparsity penalty to detect important covariates. This procedure automatically estimates the number and the locations of the change-points and the parameters in each corresponding segment. To implement the proposed approach, we devise an alternating direction method of multipliers (ADMM) algorithm. We demonstrate the convergence of the proposed ADMM algorithm in the present setting. We also establish an oracle inequality for the proposed estimator. We carry out simulation studies to evaluate the finite sample performance of the proposed method and illustrate its application on a data set.
Details
- Title: Subtitle
- Computation and analysis of change points with different jump locations in high-dimensional regression
- Creators
- Jian Huang - Univ Iowa, Dept Stat & Actuarial Sci, Iowa City, IA USAYuling Jiao - Wuhan UniversityLican Kang - Wuhan UniversityYanyan Liu - Wuhan UniversityXinfeng Yang - Jiangsu Hengrui Medicine (China)
- Resource Type
- Journal article
- Publication Details
- Statistical papers (Berlin, Germany), Vol.65(3), pp.1703-1729
- DOI
- 10.1007/s00362-023-01461-w
- ISSN
- 0932-5026
- eISSN
- 1613-9798
- Publisher
- Springer Nature
- Number of pages
- 27
- Grant note
- KLATASDSMOE 11871474; 11971362 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) DMS-1916199 / NSF; National Science Foundation (NSF)
- Language
- English
- Electronic publication date
- 07/31/2023
- Date published
- 05/2024
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984456076502771
Metrics
13 Record Views