Weighted least-squares inference for multivariate copulas based on dependence coefficients
Mazo, Gildas1 ; Girard, Stéphane1 ; Forbes, Florence1
1 Inria Grenoble Rhône-Alpes and Laboratoire Jean Kuntzmann, Inovallée, 655, av. de l’Europe, Montbonnot, 38334 Saint-Ismier Cedex, France.
ESAIM: Probability and Statistics, Tome 19 (2015), pp. 746-765.

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.

Reçu le :
DOI : 10.1051/ps/2015014
Classification : 62H12, 62F12, 60E05
Mots clés : Partial derivatives, singular component, weighted least-squares, method of moments, dependence coefficients, parametric inference, multivariate copulas
Mazo, Gildas 1 ; Girard, Stéphane 1 ; Forbes, Florence 1

1 Inria Grenoble Rhône-Alpes and Laboratoire Jean Kuntzmann, Inovallée, 655, av. de l’Europe, Montbonnot, 38334 Saint-Ismier Cedex, France. 
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Mazo, Gildas; Girard, Stéphane; Forbes, Florence. Weighted least-squares inference for multivariate copulas based on dependence coefficients. ESAIM: Probability and Statistics, Tome 19 (2015), pp. 746-765. doi : 10.1051/ps/2015014. http://www.numdam.org/articles/10.1051/ps/2015014/

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