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

Gildas Mazo (1) , Stéphane Girard (1) , Florence Forbes (1)

1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems

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.

Mots clés: partial derivatives; singular component; least-squares; method-of-moments; dependence coefficients; parametric inference; copulas; multivariate

Domaines: Théorie [stat.TH]

Gildas Mazo, Stéphane Girard, Florence Forbes. Weighted least-squares inference based on dependence coefficients for multivariate copulas. 2014. ⟨hal-00979151v3⟩