Multipartition model for multiple change point identification

Ricardo C. Pedroso (), Rosangela H. Loschi () and Fernando Andrés Quintana ()
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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

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2023, vol. 32, issue 2, No 16, 759-783

Abstract: 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.

Keywords: Random partitions; Product distribution; Temporal cluster; 62F15 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11749-023-00851-4

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