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On Finite Mixture Modeling of Change-point Processes

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An Erratum to this article was published on 16 September 2021

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Abstract

Change point estimation in standard process observed over time is an important problem in literature with applications in various fields. We study this problem in a heterogeneous population. A model-based clustering procedure relying on skewed matrix-variate mixture is proposed. It is capable of capturing the heterogeneity pattern and estimating change points from all data groups simultaneously. The appeal of such approach also lies in its flexibility to model the skewness and dependence in data with good interpretability. Two novel algorithms called matrix power mixture with abrupt change model and matrix power mixture with gradual change model are developed. The approaches are illustrated by simulation studies across a variety of settings. The models are then tested on the US crime data with promising results.

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Authors and Affiliations

  1. Department of Information Systems, Statistics, and Management Science, University of Alabama, Tuscaloosa, AL, 35487, USA

    Xuwen Zhu & Yana Melnykov

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  1. Xuwen Zhu
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  2. Yana Melnykov
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Correspondence to Xuwen Zhu.

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Zhu, X., Melnykov, Y. On Finite Mixture Modeling of Change-point Processes. J Classif 39, 3–22 (2022). https://doi.org/10.1007/s00357-021-09385-6

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  • DOI: https://doi.org/10.1007/s00357-021-09385-6

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