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Joint segmentation of multivariate Gaussian processes using mixed linear models

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  • Picard, F.
  • Lebarbier, E.
  • Budinsk√†, E.
  • Robin, S.
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    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.

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    File URL: http://www.sciencedirect.com/science/article/B6V8V-5133632-4/2/6073d4725c1fb47de417236f0da67d3c
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 55 (2011)
    Issue (Month): 2 (February)
    Pages: 1160-1170

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    Handle: RePEc:eee:csdana:v:55:y:2011:i:2:p:1160-1170

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    Web page: http://www.elsevier.com/locate/csda

    Related research

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

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    1. Henri Caussinus & Olivier Mestre, 2004. "Detection and correction of artificial shifts in climate series," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(3), pages 405-425.
    2. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    3. Dobigeon, Nicolas & Tourneret, Jean-Yves, 2007. "Joint segmentation of wind speed and direction using a hierarchical model," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5603-5621, August.
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