Sequential change point detection for high-dimensional data using nonconvex penalized quantile regression

Suthakaran Ratnasingam; Wei Ning

doi:10.1002/bimj.202000078

Sequential change point detection for high-dimensional data using nonconvex penalized quantile regression

Biom J. 2021 Mar;63(3):575-598. doi: 10.1002/bimj.202000078. Epub 2020 Nov 16.

Authors

Suthakaran Ratnasingam¹, Wei Ning^{2

3}

Affiliations

¹ Department of Mathematics, California State University, San Bernardino, CA, USA.
² Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH, USA.
³ School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, P. R. China.

PMID: 33191556
DOI: 10.1002/bimj.202000078

Abstract

In this paper, a sequential change point detection method is developed to monitor structural change in smoothly clipped absolute deviation (SCAD) penalized quantile regression (SPQR) models. The asymptotic properties of the test statistic are derived from the null and alternative hypotheses. In order to improve the performance of the SPQR method, we propose a post-SCAD penalized quantile regression estimator (P-SPQR) for high-dimensional data. We examined the finite sample properties of the proposed methods via Monte Carlo studies under different scenarios. A real data application is provided to demonstrate the effectiveness of the method.

Keywords: SCAD; change point detection; high-dimensional; quantile regression; sequential analysis.

MeSH terms

Monte Carlo Method*