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Joint segmentation of multivariate Gaussian processes using mixed linear models

Authors:	F. Picard,E. Lebarbier,E. Budinskà

Affiliation:	^a UMR CNRS-8071/INRA-1152/Université d’Évry, Évry, France ^b UMR CNRS 5558 Université Lyon 1 Claude Bernard, Université de Lyon, Laboratoire de Biométrie et Biologie Evolutive, Lyon, France ^c AgroParisTech/INRA MIA 518, Paris, France ^d Institute of Biostatistics and Analyses, Masaryk University, Brno, Czech Republic ^e BAMBOO Project, INRIA Rhône-Alpes, Saint-Martin, France

Abstract:	The joint segmentation of multiple series is considered. A mixed linear model is used to account for both covariates and correlations between signals. An estimation algorithm based on EM which involves a new dynamic programming strategy for the segmentation step is proposed. The computational efficiency of this procedure is shown and its performance is assessed through simulation experiments. Applications are presented in the field of climatic data analysis.

Keywords:	Segmentation Mixed linear model Multivariate Gaussian process Dynamic programming EM algorithm
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