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A novel dual-criterion framework for change point detection

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  • Zhang, Qiang

Abstract

In this work, we introduce a dual-criterion framework for change-point detection. This innovative approach intuitively facilitates the simultaneous determination of both the number and locations of change points, while effectively identifying and excluding outliers. Numerous one-dimensional criterion-based change point detection methods can be generalized and integrated within this novel framework.

Suggested Citation

  • Zhang, Qiang, 2024. "A novel dual-criterion framework for change point detection," Statistics & Probability Letters, Elsevier, vol. 211(C).
    • Handle: RePEc:eee:stapro:v:211:y:2024:i:c:s0167715224001019
    DOI: 10.1016/j.spl.2024.110132
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    References listed on IDEAS

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    1. David S. Matteson & Nicholas A. James, 2014. "A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 334-345, March.
    2. Harchaoui, Z. & Lévy-Leduc, C., 2010. "Multiple Change-Point Estimation With a Total Variation Penalty," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1480-1493.
    3. Fryzlewicz, Piotr, 2014. "Wild binary segmentation for multiple change-point detection," LSE Research Online Documents on Economics 57146, London School of Economics and Political Science, LSE Library.
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