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Activity Number: 275 - Advances in Dependence Modeling Through Copulas
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: SSC
Abstract #326715
Title: Semiparametric Inference for Copulas of Mixed Data
Author(s): Bruno Remillard* and Christian Genest and Johanna G. Neslehova
Companies: HEC Montreal and McGill University and McGill University
Keywords: Inference; Copula; Mixed data

Inference methods are proposed for the estimation of the parameter of a copula family when the unknown marginal distributions are mixtures of discrete and absolutely continuous distribution functions. Under smoothness assumptions, the estimation errors are shown to be Gaussian and their variance can be estimated.

Authors who are presenting talks have a * after their name.

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