Constraining kernel estimators in semiparametric copula mixture models

Gildas Mazo (1, 2) , Yaroslav Averyanov (3)

1 INRA - Institut National de la Recherche Agronomique
2 Université Paris-Saclay
3 MODAL - MOdel for Data Analysis and Learning

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.

Mots clés: copula; kernel; nonparametric; semiparametric; mixture model; clustering

Domaines: Statistiques [stat]; Méthodologie [stat.ME]

Gildas Mazo, Yaroslav Averyanov. Constraining kernel estimators in semiparametric copula mixture models. 2019. ⟨hal-01774629v3⟩