Joint segmentation of multivariate Gaussian processes using
mixed linear models.

J 2011

Joint segmentation of multivariate Gaussian processes using mixed linear models.

PICARD, Franck; Emily LEBARBIER; Eva BUDINSKÁ and Stephane ROBIN

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

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

References:

Odkaz na stiahnutie článku

Impact factor

Impact factor: 1.028

RIV identification code

RIV/00216224:14110/11:00051559

Organization unit

Faculty of Medicine

DOI

https://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

Abstract

In the original language

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.

Cite

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://doi.org/10.1016/j.csda.2010.09.015.

@article{910438,
   author = {Picard, Franck and Lebarbier, Emily and Budinská, Eva and Robin, Stephane},
   article_number = {2},
   doi = {https://doi.org/10.1016/j.csda.2010.09.015},
   keywords = {Segmentation; Mixed linear model; Multivariate Gaussian process; Dynamic programming; EM algorithm},
   language = {eng},
   issn = {0167-9473},
   journal = {Computational Statistics & Data Analysis},
   title = {Joint segmentation of multivariate Gaussian processes using mixed linear models.},
   url = {http://pbil.univ-lyon1.fr/members/fpicard/franckpicard_fichiers/pdf/PLB11.pdf},
   volume = {55},
   year = {2011}
}

TY  - JOUR
ID  - 910438
AU  - Picard, Franck - Lebarbier, Emily - Budinská, Eva - Robin, Stephane
PY  - 2011
TI  - Joint segmentation of multivariate Gaussian processes using mixed linear models.
JF  - Computational Statistics & Data Analysis
VL  - 55
IS  - 2
SP  - 1160-1170
EP  - 1160-1170
PB  - ELSEVIER
SN  - 01679473
KW  - Segmentation
KW  - Mixed linear model
KW  - Multivariate Gaussian process
KW  - Dynamic programming
KW  - EM algorithm
UR  - http://pbil.univ-lyon1.fr/members/fpicard/franckpicard_fichiers/pdf/PLB11.pdf
L2  - http://pbil.univ-lyon1.fr/members/fpicard/franckpicard_fichiers/pdf/PLB11.pdf
N2  - 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.
ER  -

PICARD, Franck; Emily LEBARBIER; Eva BUDINSKÁ and Stephane ROBIN. Joint segmentation of multivariate Gaussian processes using mixed linear models. \textit{Computational Statistics \&{} Data Analysis}. ELSEVIER, 2011, vol.~55, No~2, p.~1160-1170. ISSN~0167-9473. Available from: https://doi.org/10.1016/j.csda.2010.09.015.

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