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Journal Articles ESAIM: Probability and Statistics Year : 2015

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

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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Dates and versions

hal-00979151 , version 1 (15-04-2014)
hal-00979151 , version 2 (05-05-2014)
hal-00979151 , version 3 (17-08-2014)
hal-00979151 , version 4 (13-11-2014)
hal-00979151 , version 5 (13-11-2014)
hal-00979151 , version 6 (22-10-2015)

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Gildas Mazo, Stéphane Girard, Florence Forbes. Weighted least-squares inference for multivariate copulas based on dependence coefficients. ESAIM: Probability and Statistics, 2015, 19, pp.746 - 765. ⟨10.1051/ps/2015014⟩. ⟨hal-00979151v6⟩
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