Robust change point detection method via adaptive LAD-LASSO

被引：17

作者：

Li, Qiang ^{[1
]}

Wang, Liming ^{[2
]}

机构：

[1] Taishan Univ, Sch Math & Stat, Tai An, Shandong, Peoples R China

[2] Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai, Peoples R China

来源：

STATISTICAL PAPERS | 2020年 / 61卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Change point detection; Adaptive LAD-LASSO; Variable selection; Robustness; Screening; LEAST-SQUARES ESTIMATION; VARIABLE SELECTION; REGRESSION SHRINKAGE; GENERALIZED METHODS; NOISE REMOVAL; SOLVERS; NUMBER; MODEL;

D O I：

10.1007/s00362-017-0927-3

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Change point problem is one of the hot issues in statistics, econometrics, signal processing and so on. LAD estimator is more robust than OLS estimator, especially when datasets subject to heavy tailed errors or outliers. LASSO is a popular choice for shrinkage estimation. In the paper, we combine the two classical ideas together to put forward a robust detection method via adaptive LAD-LASSO to estimate change points in the mean-shift model. The basic idea is converting the change point estimation problem into variable selection problem with penalty. An enhanced two-step procedure is proposed. Simulation and a real example show that the novel method is really feasible and the fast and effective computation algorithm is easier to realize.

引用

页码：109 / 121

页数：13

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