Constraining kernel estimators in semiparametric copula mixture models

Mazo, Gildas; Averyanov, Yaroslav

Type de document :

Rapport de recherche

Titre :

Constraining kernel estimators in semiparametric copula mixture models

Auteur(s) :

Mazo, Gildas [Auteur]
Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] [MaIAGE]
Averyanov, Yaroslav [Auteur]
MOdel for Data Analysis and Learning [MODAL]

Date de publication :

2018

Mot(s)-clé(s) :

copula
semiparametric
nonparametric
mixture model

Mot(s)-clé(s) en anglais :

kernel
clustering

Discipline(s) HAL :

Mathématiques [math]
Informatique [cs]
Sciences du Vivant [q-bio]

Résumé en anglais : [en]

This paper presents a novel algorithm for performing inference and/or clustering in semiparametric copula-based mixture models. The algorithm replaces the standard kernel density estimator by a weighted version that permits ...
Lire la suite >This paper presents a novel algorithm for performing inference and/or clustering in semiparametric copula-based mixture models. The algorithm replaces the standard kernel density estimator by a weighted version that permits to take into account the constraints put on the underlying marginal densities. Lower misclassification error rates and better estimates are obtained on simulations. The pointwise consistency of the weighted kernel density estimator is established under an assumption on the rate of convergence of the sample maximum.Lire moins >

Langue :

Anglais

Commentaire :

Working paper Il s'agit d'un preprint qui a été soumis dans une revue à comité de lecture

Collections :

Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL

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