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Constraining kernel estimators in semiparametric copula mixture models

Author:
Mazo, Gildas, Averyanov, Yaroslav
Source:
Computational statistics & data analysis 2019 v.138 pp. 170-189
ISSN:
0167-9473
Subject:
algorithms, models
Abstract:
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.
Agid:
6379474