Robust change point detection method via adaptive LAD-LASSO

• Published in: Statistical papers (Berlin, Germany) Vol. 61; no. 1; pp. 109 - 121
• Main Authors: Li, Qiang, Wang, Liming
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2020; Springer Nature B.V
• Subjects: Algorithms; Basic converters; Change detection; Computer simulation; Econometrics; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Finance; Insurance; Management; Mathematics and Statistics; Operations Research/Decision Theory; Outliers (statistics); Probability Theory and Stochastic Processes; Regular Article; Robustness; Signal processing; Statistics; Statistics for Business; Screening; Change point detection; 62J07; Robustness; 62F35; Adaptive LAD-LASSO; Variable selection
• DOI: 10.1007/s00362-017-0927-3
• ISSN: 0932-5026

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.