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Conference Papers Year : 2013

A copula to handle tail dependence in high dimension

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Abstract

The concept of copula is a useful tool to model multivariate distributions but the construction of tail dependent high dimensional copulas remains a challenging problem. We propose a new copula constructed by introducing a latent factor. Conditional independence with respect to this factor and the use of a nonparametric class of bivariate copulas lead to interesting properties like explicitness, flexibility and parsimony. We propose a pairwise moment-based inference procedure and prove asymptotic normality of our estimator. Finally we illustrate our model on simulated and real data.
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Dates and versions

hal-00915690 , version 1 (09-12-2013)

Identifiers

  • HAL Id : hal-00915690 , version 1

Cite

Gildas Mazo, Stéphane Girard, Florence Forbes. A copula to handle tail dependence in high dimension. ERCIM 2013 - 6th International Conference of the ERCIM WG on Computational and Methodological Statistics, Dec 2013, London, United Kingdom. ⟨hal-00915690⟩
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