Constraining kernel estimators in semiparametric copula mixture models

Gildas Mazo and Yaroslav Averyanov

Computational Statistics & Data Analysis, 2019, vol. 138, issue C, 170-189

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.

Keywords: Copula; Kernel; Semiparametric; Nonparametric; Mixture model; Clustering (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:138:y:2019:i:c:p:170-189

DOI: 10.1016/j.csda.2019.04.010

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