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Asymptotic distribution-free change-point detection based on interpoint distances for high-dimensional data

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  • Jun Li

Abstract

Recent advances have greatly facilitated the collection of high-dimensional data in many fields. Often the dimension of the data is much larger than the sample size, the so-called high dimension, low sample size setting. One important research problem is how to develop efficient change-point detection procedures for this new setting. Thanks to their simplicity of computation, interpoint distance-based procedures provide a potential solution to this problem. However, most of the existing distance-based procedures fail to fully utilise interpoint distances, and as a result, they suffer significant loss of power. In this paper, we propose a new asymptotic distribution-free distance-based change-point detection procedure for the high dimension, low sample size setting. The proposed procedure is proven to be consistent for detecting both location and scale changes and can also provide a consistent estimator for the change-point. Our simulation study and real data analysis show that it significantly outperforms the existing methods across a variety of settings.

Suggested Citation

  • Jun Li, 2020. "Asymptotic distribution-free change-point detection based on interpoint distances for high-dimensional data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 32(1), pages 157-184, January.
    • Handle: RePEc:taf:gnstxx:v:32:y:2020:i:1:p:157-184
    DOI: 10.1080/10485252.2019.1710505
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    Cited by:

    1. Ping‐Shou Zhong, 2023. "Homogeneity tests of covariance for high‐dimensional functional data with applications to event segmentation," Biometrics, The International Biometric Society, vol. 79(4), pages 3332-3344, December.

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