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Multipartition model for multiple change point identification

Author

Listed:
  • Ricardo C. Pedroso

    (Universidade Federal de Minas Gerais)

  • Rosangela H. Loschi

    (Universidade Federal de Minas Gerais)

  • Fernando Andrés Quintana

    (Pontificia Universidad Católica de Chile and Millennium Nucleus Center for the Discovery of Structures in Complex Data)

Abstract

The product partition model (PPM) is widely used for detecting multiple change points. Because changes in different parameters may occur at different times, the PPM fails to identify which parameters experienced the changes. To solve this limitation, we introduce a multipartition model to detect multiple change points occurring in several parameters. It assumes that changes experienced by each parameter generate a different random partition along the time axis, which facilitates identifying those parameters that changed and the time when they do so. We apply our model to detect multiple change points in Normal means and variances. Simulations and data illustrations show that the proposed model is competitive and enriches the analysis of change point problems.

Suggested Citation

  • Ricardo C. Pedroso & Rosangela H. Loschi & Fernando Andrés Quintana, 2023. "Multipartition model for multiple change point identification," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 759-783, June.
    • Handle: RePEc:spr:testjl:v:32:y:2023:i:2:d:10.1007_s11749-023-00851-4
    DOI: 10.1007/s11749-023-00851-4
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    References listed on IDEAS

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    1. Lajos Horváth & Gregory Rice, 2014. "Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 219-255, June.
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