Joint segmentation of multivariate Gaussian processes using mixed linear models
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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- 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.
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- Tom Doan, "undated". "BAIPERRON: RATS procedure to perform Bai-Perron Test for Multiple Structural Changes," Statistical Software Components RTS00013, Boston College Department of Economics.
- 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. Full references (including those not matched with items on IDEAS)
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