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October 2009 Multivariate Archimedean copulas, d-monotone functions and 1-norm symmetric distributions
Alexander J. McNeil, Johanna Nešlehová
Ann. Statist. 37(5B): 3059-3097 (October 2009). DOI: 10.1214/07-AOS556


It is shown that a necessary and sufficient condition for an Archimedean copula generator to generate a d-dimensional copula is that the generator is a d-monotone function. The class of d-dimensional Archimedean copulas is shown to coincide with the class of survival copulas of d-dimensional 1-norm symmetric distributions that place no point mass at the origin. The d-monotone Archimedean copula generators may be characterized using a little-known integral transform of Williamson [Duke Math. J. 23 (1956) 189–207] in an analogous manner to the well-known Bernstein–Widder characterization of completely monotone generators in terms of the Laplace transform. These insights allow the construction of new Archimedean copula families and provide a general solution to the problem of sampling multivariate Archimedean copulas. They also yield useful expressions for the d-dimensional Kendall function and Kendall’s rank correlation coefficients and facilitate the derivation of results on the existence of densities and the description of singular components for Archimedean copulas. The existence of a sharp lower bound for Archimedean copulas with respect to the positive lower orthant dependence ordering is shown.


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Alexander J. McNeil. Johanna Nešlehová. "Multivariate Archimedean copulas, d-monotone functions and 1-norm symmetric distributions." Ann. Statist. 37 (5B) 3059 - 3097, October 2009.


Published: October 2009
First available in Project Euclid: 17 July 2009

zbMATH: 1173.62044
MathSciNet: MR2541455
Digital Object Identifier: 10.1214/07-AOS556

Primary: 62E10 , 62H05 , 62H20
Secondary: 60E05

Keywords: Archimedean copula , dependence ordering , d-monotone function , frailty model , ℓ_1-norm symmetric distribution , Laplace transform , Stochastic simulation , Williamson d-transform

Rights: Copyright © 2009 Institute of Mathematical Statistics

Vol.37 • No. 5B • October 2009
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