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Rank-based multiple change-point detection

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  • Yunlong Wang
  • Zhaojun Wang
  • Xuemin Zi

Abstract

A nonparametric procedure is proposed to estimate multiple change-points of location changes in a univariate data sequence by using ranks instead of the raw data. While existing rank-based multiple change-point detection methods are mostly based on sequential tests, we treat it as a model selection problem. We derive the corresponding Schwarz’s information criterion for rank-statistics, theoretically prove the consistency of the change-point estimator and use a pruned dynamic programing algorithm to achieve the change-point estimator. Simulation studies show our method’s robustness, effectiveness and efficiency in detecting mean-changes. We also apply the method to a gene dataset as an illustration.

Suggested Citation

  • Yunlong Wang & Zhaojun Wang & Xuemin Zi, 2020. "Rank-based multiple change-point detection," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(14), pages 3438-3454, July.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:14:p:3438-3454
    DOI: 10.1080/03610926.2019.1589515
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    Cited by:

    1. Zhang, Wenjia & Wu, Yulin & Deng, Guobang, 2024. "Social and spatial disparities in individuals’ mobility response time to COVID-19: A big data analysis incorporating changepoint detection and accelerated failure time models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 184(C).

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