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arXiv · 2511.18432

Change-Point Detection With Multivariate Repeated Measures

Serim Han·Jingru Zhang·Hoseung Song
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Abstract

Graph-based methods have shown particular strengths in change-point detection (CPD) tasks for high-dimensional nonparametric settings. However, existing CPD research has rarely addressed data with repeated measurements or local group structures. A common treatment is to average repeated measurements, which can result in the loss of important within-individual information. In this paper, we propose a new graph-based method for detecting change-points in data with repeated measurements or local structures by incorporating both within-individual and between-individual information. Analytical approximations to the significance of the proposed statistics are derived, enabling efficient computation of p-values for the combined test statistic. We also establish consistency of the proposed test and the estimated change-point location. The proposed method effectively detects change-points across a wide range of alternatives, particularly when within-individual differences are present. The new method is illustrated through an analysis of the New York City taxi dataset.

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Serim Han, Jingru Zhang, Hoseung Song. 2025-11-23. Change-Point Detection With Multivariate Repeated Measures. https://arxiv.org/abs/2511.18432

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