Volume 19, 2015
|Page(s)||746 - 765|
|Published online||11 December 2015|
Weighted least-squares inference for multivariate copulas based on dependence coefficients
Inria Grenoble Rhône-Alpes and Laboratoire Jean Kuntzmann, Inovallée, 655, av. de l’Europe, Montbonnot, 38334 Saint-Ismier Cedex, France.
Received: 29 September 2014
Revised: 21 October 2015
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.
Mathematics Subject Classification: 62H12 / 62F12 / 60E05
Key words: Partial derivatives / singular component / weighted least-squares / method of moments / dependence coefficients / parametric inference / multivariate copulas
© EDP Sciences, SMAI, 2015
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.