Rank-based multiple change-point detection

Yunlong Wang, Zhaojun Wang and Xuemin Zi

Communications in Statistics - Theory and Methods, 2020, vol. 49, issue 14, 3438-3454

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.

Date: 2020
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DOI: 10.1080/03610926.2019.1589515

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