PICARD, Franck, Emily LEBARBIER, Eva BUDINSKÁ and Stephane ROBIN. Joint segmentation of multivariate Gaussian processes using mixed linear models. Computational Statistics & Data Analysis. ELSEVIER, 2011, vol. 55, No 2, p. 1160-1170. ISSN 0167-9473. Available from: https://dx.doi.org/10.1016/j.csda.2010.09.015.
Other formats:   BibTeX LaTeX RIS
Basic information
Original name Joint segmentation of multivariate Gaussian processes using mixed linear models.
Authors PICARD, Franck (250 France, guarantor), Emily LEBARBIER (250 France), Eva BUDINSKÁ (703 Slovakia, belonging to the institution) and Stephane ROBIN (250 France).
Edition Computational Statistics & Data Analysis, ELSEVIER, 2011, 0167-9473.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10103 Statistics and probability
Country of publisher United Kingdom of Great Britain and Northern Ireland
Confidentiality degree is not subject to a state or trade secret
WWW Odkaz na stiahnutie článku
Impact factor Impact factor: 1.028
RIV identification code RIV/00216224:14110/11:00051559
Organization unit Faculty of Medicine
Doi http://dx.doi.org/10.1016/j.csda.2010.09.015
UT WoS 000284976600019
Keywords in English Segmentation; Mixed linear model; Multivariate Gaussian process; Dynamic programming; EM algorithm
Tags International impact
Changed by Changed by: Mgr. Michal Petr, učo 65024. Changed: 12/4/2012 09:21.
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.
PrintDisplayed: 27/9/2024 00:22