Robust change point detection method via adaptive LAD-LASSO

被引：15

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

共 50 条

[1] Robust change point detection method via adaptive LAD-LASSO
Qiang Li
Liming Wang
[J]. Statistical Papers, 2020, 61 : 109 - 121
[2] Outlier detection and robust variable selection via the penalized weighted LAD-LASSO method
Jiang, Yunlu
Wang, Yan
Zhang, Jiantao
Xie, Baojian
Liao, Jibiao
Liao, Wenhui
[J]. JOURNAL OF APPLIED STATISTICS, 2021, 48 (02) : 234 - 246
[3] Weighted LAD-LASSO method for robust parameter estimation and variable selection in regression
Arslan, Olcay
[J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2012, 56 (06) : 1952 - 1965
[4] Robust regression shrinkage and consistent variable selection through the LAD-lasso
Wang, Hansheng
Li, Guodong
Jiang, Guohua
[J]. JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 2007, 25 (03) : 347 - 355
[5] Modelling old-age retirement: An adaptive multi-outcome LAD-lasso regression approach
Lahderanta, Tero
Salonen, Janne
Mottonen, Jyrki
Sillanpaa, Mikko J.
[J]. INTERNATIONAL SOCIAL SECURITY REVIEW, 2022, 75 (01) : 3 - 29
[6] Reducing bias and mitigating the influence of excess of zeros in regression covariates with multi-outcome adaptive LAD-lasso
Mottonen, Jyrki
Lahderanta, Tero
Salonen, Janne
Sillanpaa, Mikko J.
[J]. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2024, 53 (13) : 4730 - 4744
[7] Test by adaptive LASSO quantile method for real-time detection of a change-point
Ciuperca, Gabriela
[J]. METRIKA, 2018, 81 (06) : 689 - 720
[8] Test by adaptive LASSO quantile method for real-time detection of a change-point
Gabriela Ciuperca
[J]. Metrika, 2018, 81 : 689 - 720
[9] Real time change-point detection in a model by adaptive LASSO and CUSUM
Ciuperca, Gabriela
[J]. JOURNAL OF THE SFDS, 2015, 156 (04): : 113 - 132
[10] Adversarially Robust Change Point Detection
Li, Mengchu
Yu, Yi
[J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021), 2021, 34

← 1 2 3 4 5 →