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Sequential change point detection for high‐dimensional data using nonconvex penalized quantile regression
Author(s) -
Ratnasingam Suthakaran,
Ning Wei
Publication year - 2021
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.202000078
Subject(s) - scad , quantile , quantile regression , mathematics , estimator , test statistic , statistics , monte carlo method , statistic
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.