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Product partition latent variable model for multiple change-point detection in multivariate data

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  • Gift Nyamundanda
  • Avril Hegarty
  • Kevin Hayes

Abstract

The product partition model (PPM) is a well-established efficient statistical method for detecting multiple change points in time-evolving univariate data. In this article, we refine the PPM for the purpose of detecting multiple change points in correlated multivariate time-evolving data. Our model detects distributional changes in both the mean and covariance structures of multivariate Gaussian data by exploiting a smaller dimensional representation of correlated multiple time series. The utility of the proposed method is demonstrated through experiments on simulated and real datasets.

Suggested Citation

  • Gift Nyamundanda & Avril Hegarty & Kevin Hayes, 2015. "Product partition latent variable model for multiple change-point detection in multivariate data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(11), pages 2321-2334, November.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2321-2334
    DOI: 10.1080/02664763.2015.1029444
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    Cited by:

    1. 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.

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