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Robust change point detection method via adaptive LAD-LASSO

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 t... Full description

Journal Title: Statistical papers (Berlin Germany), 2017-06-30, Vol.61 (1), p.109-121
Main Author: Li, Qiang
Other Authors: Wang, Liming
Format: Electronic Article Electronic Article
Language: English
Subjects:
Algorithms
Analysis
Basic converters
Change detection
Computer simulation
Econometrics
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Finance
Insurance
Management
Mathematics and Statistics
Methods
Operations Research/Decision Theory
Outliers (statistics)
Probability Theory and Stochastic Processes
Regular Article
Robustness
Signal processing
Statistics
Statistics for Business
Publisher: Berlin/Heidelberg: Springer Berlin Heidelberg
ID: ISSN: 0932-5026
Zum Text:
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recordid: cdi_springer_journals_10_1007_s00362_017_0927_3
title: Robust change point detection method via adaptive LAD-LASSO
format: Article
creator:
  • Li, Qiang
  • Wang, Liming
subjects:
  • Algorithms
  • Analysis
  • Basic converters
  • Change detection
  • Computer simulation
  • Econometrics
  • Economic Theory/Quantitative Economics/Mathematical Methods
  • Economics
  • Finance
  • Insurance
  • Management
  • Mathematics and Statistics
  • Methods
  • Operations Research/Decision Theory
  • Outliers (statistics)
  • Probability Theory and Stochastic Processes
  • Regular Article
  • Robustness
  • Signal processing
  • Statistics
  • Statistics for Business
ispartof: Statistical papers (Berlin, Germany), 2017-06-30, Vol.61 (1), p.109-121
description: 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.
language: eng
source:
identifier: ISSN: 0932-5026
fulltext: no_fulltext
issn:
  • 0932-5026
  • 1613-9798
url: Link


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descriptionChange 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.
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subjectAlgorithms ; Analysis ; Basic converters ; Change detection ; Computer simulation ; Econometrics ; Economic Theory/Quantitative Economics/Mathematical Methods ; Economics ; Finance ; Insurance ; Management ; Mathematics and Statistics ; Methods ; Operations Research/Decision Theory ; Outliers (statistics) ; Probability Theory and Stochastic Processes ; Regular Article ; Robustness ; Signal processing ; Statistics ; Statistics for Business
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descriptionChange 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.
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abstractChange 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.
copBerlin/Heidelberg
pubSpringer Berlin Heidelberg
doi10.1007/s00362-017-0927-3