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