Constraining kernel estimators in semiparametric copula mixture models

Mazo, Gildas; Averyanov, Yaroslav

Document type :

Rapport de recherche

Title :

Constraining kernel estimators in semiparametric copula mixture models

Author(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]

Publication date :

2018

Keyword(s) :

copula
semiparametric
nonparametric
mixture model

English keyword(s) :

kernel
clustering

HAL domain(s) :

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

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Comment :

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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