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 a Stephane ROBIN

Základní údaje

Originální název

Joint segmentation of multivariate Gaussian processes using mixed linear models.

Autoři

PICARD, Franck (250 Francie, garant); Emily LEBARBIER (250 Francie); Eva BUDINSKÁ (703 Slovensko, domácí) a Stephane ROBIN (250 Francie)

Vydání

Computational Statistics & Data Analysis, ELSEVIER, 2011, 0167-9473

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10103 Statistics and probability

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Odkaz na stiahnutie článku

Impakt faktor

Impact factor: 1.028

Kód RIV

RIV/00216224:14110/11:00051559

Organizační jednotka

Lékařská fakulta

DOI

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

UT WoS

000284976600019

Klíčová slova anglicky

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

Příznaky

Mezinárodní význam

Změněno: 12. 4. 2012 09:21, Mgr. Michal Petr

Anotace

V originále

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.

Citovat

PICARD, Franck; Emily LEBARBIER; Eva BUDINSKÁ a Stephane ROBIN. Joint segmentation of multivariate Gaussian processes using mixed linear models. Computational Statistics & Data Analysis. ELSEVIER, 2011, roč. 55, č. 2, s. 1160-1170. ISSN 0167-9473. Dostupné z: 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 a Stephane ROBIN. Joint segmentation of multivariate Gaussian processes using mixed linear models. \textit{Computational Statistics \&{} Data Analysis}. ELSEVIER, 2011, roč.~55, č.~2, s.~1160-1170. ISSN~0167-9473. Dostupné z: https://doi.org/10.1016/j.csda.2010.09.015.

Přehled o publikaci