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Joint segmentation of multivariate Gaussian processes using mixed linear models

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. (C) 2010 Elsevier B.V. All rights reserved.
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Submitted on : Wednesday, June 4, 2014 - 3:32:24 PM
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Franck Picard, E. Lebarbier, E. Budinska, Stephane Robin. Joint segmentation of multivariate Gaussian processes using mixed linear models. Computational Statistics and Data Analysis, Elsevier, 2011, 55 (2), pp.1160 - 1170. ⟨10.1016/j.csda.2010.09.015⟩. ⟨hal-01001452⟩

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