Geodesic
Weighted least-squares inference for multivariate copulas based on dependence coefficients
Mazo, Gildas1 idref ; Girard, Stéphane1 idref ; Forbes, Florence1 idref
1Inria 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

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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
Keywords: 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. 
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
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     title = {Weighted least-squares inference for multivariate copulas based on dependence coefficients},
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     pages = {746--765},
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