Titre :

Model-based clustering for multivariate functional data

Auteur(s) :

Jacques, Julien [Auteur]
Preda, Cristian [Auteur]

Titre de la revue :

Computational Statistics and Data Analysis

Numéro :

71

Pagination :

92-106

Éditeur :

Elsevier

Date de publication :

2014-06-01

ISSN :

0167-9473

Mot(s)-clé(s) :

Multivariate functional data
Density approximation
Model-based clustering
Multivariate functional principal component analysis
EM algorithm

Discipline(s) HAL :

Mathématiques [math]/Statistiques [math.ST]

Résumé en anglais : [en]

This paper proposes the first model-based clustering algorithm for multivariate functional data. After introducing multivariate functional principal components analysis (MFPCA), a parametric mixture model, {based on the ...
Lire la suite >This paper proposes the first model-based clustering algorithm for multivariate functional data. After introducing multivariate functional principal components analysis (MFPCA), a parametric mixture model, {based on the assumption of normality of the principal components}, is defined and estimated by an EM-like algorithm. The main advantage of the proposed model is its ability to take into account the dependence among curves. Results on simulated and real datasets show the efficiency of the proposed method.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

CNRS
Université de Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Date de dépôt :

2020-06-08T14:11:37Z