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.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (Grant No. 71271128 and Grant No. 71540038), the Natural Science Foundation of Shandong Province in China (Grant No. ZR2014AL006) and the Talent Research Fund of Taishan University (Grant No. Y-01-2016002). The authors thank the Editor and the three Referees for their very helpful comments and suggestions.
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Li, Q., Wang, L. Robust change point detection method via adaptive LAD-LASSO. Stat Papers 61, 109–121 (2020). https://doi.org/10.1007/s00362-017-0927-3
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DOI: https://doi.org/10.1007/s00362-017-0927-3