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

Gift Nyamundanda, Avril Hegarty and Kevin Hayes

Journal of Applied Statistics, 2015, vol. 42, issue 11, 2321-2334

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.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:42:y:2015:i:11:p:2321-2334

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DOI: 10.1080/02664763.2015.1029444

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