Rank-based multiple change-point detection

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.

Keywords:

• Multiple change-points
• rank method
• SIC
• robustness

Acknowledgments

The authors are grateful to the referee, Associate Editor, and Editor for their insightful comments that have significantly improved the article.