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Robust change point detection method via adaptive LAD-LASSO

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  • Qiang Li

    (Taishan University)

  • Liming Wang

    (Shanghai University of Finance and Economics)

Abstract

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.

Suggested Citation

  • Qiang Li & Liming Wang, 2020. "Robust change point detection method via adaptive LAD-LASSO," Statistical Papers, Springer, vol. 61(1), pages 109-121, February.
    • Handle: RePEc:spr:stpapr:v:61:y:2020:i:1:d:10.1007_s00362-017-0927-3
    DOI: 10.1007/s00362-017-0927-3
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    References listed on IDEAS

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    2. Junwei Hu & Lihong Wang, 2023. "A weighted U-statistic based change point test for multivariate time series," Statistical Papers, Springer, vol. 64(3), pages 753-778, June.

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