Christian Genest
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Cahiers du GERAD
Traditionally, claim counts and amounts are assumed to be independent in non-life insurance. This paper explores how this oft unwarranted assumption can be r...
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In order to determine the risk capital for their aggregate portfolio, property and casualty insurance companies must fit a multivariate model to the loss tri...
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Two bivariate extensions of the Skellam distribution were proposed by Genest and Mesfioui (2014), who also derived moment estimators for their dependence par...
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Many proposals have been made recently for goodness-of-fit testing of copula models. After reviewing them briefly, the authors concentrate on omnibus proced...
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This paper proposes new tests of randomness for innovations of a large class of time series models. These tests are based on functionals of empirical proces...
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In testing that a particular distribution <img src="/cgi-bin/mimetex.cgi?P"> belongs to a parameterized family <img src="/cgi-bin/mimetex.cgi?\cal{P}">, one ...
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Deheuvels (1981a,b,c) and Genest and Rémillard (2004) have shown that powerful rank tests of multivariate independence can be based on combinations of asymp...
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Deheuvels proposed a rank test of independence based on a Cramér–von Mises functional of the empirical copula process. Using a general result on the asympto...
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Rank-based estimators were proposed by Clayton (1978) and Oakes (1982) for the association parameter in the bivariate gamma frailty model. The joint asympto...
BibTeX referenceGoodness-of-fit Procedures for Copula Models Based on the Integral Probability Transformation
An approach is suggested for testing whether the dependence structure of a random sample of multivariate data is appropriately modelled by a given family of ...
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Exploiting an overlooked observation of Blum, Kiefer & Rosenblatt (1961), Dugué (1975) and Deheuvels (1981a) described a decomposition of empirical distribu...
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This paper introduces new rank-based statistics for testing against serial dependence in a univariate time series context. These Kolmogorov-Smirnov and Cram...
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