Constraining kernel estimators in semiparametric copula mixture models

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

1 MaIAGE - Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas]
2 Université Paris Saclay (COmUE)
3 MODAL - MOdel for Data Analysis and Learning

A novel algorithm for performing inference and/or clustering in semiparametric copula-based mixture models is presented. The standard kernel density estimator is replaced 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: Kernel; Clustering

Mots clés: Copula; Semiparametric; Nonparametric; Mixture model

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

Gildas Mazo, Yaroslav Averyanov. Constraining kernel estimators in semiparametric copula mixture models. Computational Statistics and Data Analysis, 2019, 138, pp.170-189. ⟨10.1016/j.csda.2019.04.010⟩. ⟨hal-02620478⟩