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Weighted least-squares inference for multivariate copulas based on dependence coefficients

Gildas Mazo 1 Stephane Girard 1 Florence Forbes 1
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : In this paper, we address the issue of estimating the parameters of general multivariate copulas, that is, copulas whose partial derivatives may not exist. To this aim, we consider a weighted least-squares estimator based on dependence coefficients, and establish its consistency and asymptotic normality. The estimator's performance on finite samples is illustrated on simulations and a real dataset.
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Gildas Mazo, Stephane Girard, Florence Forbes. Weighted least-squares inference for multivariate copulas based on dependence coefficients. ESAIM: Probability and Statistics, EDP Sciences, 2015, 19, pp.746 - 765. ⟨10.1051/ps/2015014⟩. ⟨hal-00979151v6⟩

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