On clustering procedures and nonparametric mixture estimation - Université de Rennes Accéder directement au contenu
Article Dans Une Revue Electronic Journal of Statistics Année : 2015

On clustering procedures and nonparametric mixture estimation

Résumé

This paper deals with nonparametric estimation of conditional den-sities in mixture models in the case when additional covariates are available. The proposed approach consists of performing a prelim-inary clustering algorithm on the additional covariates to guess the mixture component of each observation. Conditional densities of the mixture model are then estimated using kernel density estimates ap-plied separately to each cluster. We investigate the expected L 1 -error of the resulting estimates and derive optimal rates of convergence over classical nonparametric density classes provided the clustering method is accurate. Performances of clustering algorithms are measured by the maximal misclassification error. We obtain upper bounds of this quantity for a single linkage hierarchical clustering algorithm. Lastly, applications of the proposed method to mixture models involving elec-tricity distribution data and simulated data are presented.
Fichier principal
Vignette du fichier
main_arxiv.pdf (627.64 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01113848 , version 1 (06-02-2015)

Identifiants

Citer

Stéphane Auray, Nicolas Klutchnikoff, Laurent Rouvière. On clustering procedures and nonparametric mixture estimation. Electronic Journal of Statistics , 2015, 9, pp.266-297. ⟨10.1214/15-EJS995⟩. ⟨hal-01113848⟩
474 Consultations
97 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More