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Activity Number:
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345
- Time Series and High-Dimensional Data
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #313976
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Title:
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High-Dimensional Change Point Detection for Heteroscedastic Data Using Bootstrap
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Author(s):
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Teng Wu* and Xiaofeng Shao and Stanislav Volgushev
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Companies:
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University of Illinois, Urbana Champaign and University of Illinois at Urbana-Champaign and University of Toronto
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Keywords:
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Change point detection;
High dimensional data;
U-statistics;
Bootstrap;
heteroscedasticity
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Abstract:
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In this paper, we propose a change point detection method testing mean shift for high dimensional observations with unknown heteroscedasticity. The proposed tests target a dense alternative and a wild bootstrap procedure is used to estimate the unknown limiting distribution. The bootstrap test is free of tuning parameters and we derive bootstrap consistency under the null. We extend the theory results to testing multiple change points and provide the justification for the size and power. For estimation of unknown change point locations, we utilize the wild binary segmentation algorithm. Empirical studies shows that our methods have the correct size and better power compared with existing approach when heteroscedasticity exists.
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Authors who are presenting talks have a * after their name.
