Spatial rank-based high-dimensional change point detection via random integration

Lei Shu, Yu Chen, Weiping Zhang and Xueqin Wang

Journal of Multivariate Analysis, 2022, vol. 189, issue C

Abstract: Detecting change points is an important task to identify an abrupt and significant change in the data generating process. Traditional change point detection methods are not applicable in high-dimensional situations due to many obstacles, such as the requirement of normality or the estimation of the covariance matrix. This paper presented a novel nonparametric method to overcome such issues by using the random integration with spatial ranks, which is tailored to high-dimensional change point detection problems. The proposed method is a unified framework that includes and extends many existing methods and can effectively handle high-dimensional non-normal data, whose asymptotic properties are established under mild conditions. In addition, we developed a computationally efficient algorithm to calculate the rejection thresholds and an effective post-signal diagnostic procedure to identify the potential directions. Finally, numerical studies together with real data examples demonstrated that the proposed method can identify the change point efficiently.

Keywords: Off-line retrospective; On-line monitoring; Random integration; Spatial signs and ranks (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.jmva.2021.104942

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