TITLE: Inference in multivariate Archimedean copula models

SPEAKER:  Johanna Neslehova

ABSTRACT:

Archimedean copulas are popular dependence structures which may be regarded as extensions of shared multiplicative frailty models, frequently used in biostatistics. This talk is concerned with new rank-based estimators for these copulas. The approach stems from a recent representation of these copulas as the survival copulas of simplex distributions. The procedures are based on a reconstruction of the radial part of the simplex distribution from the so-called Kendall distribution, which arises through the multivariate probability integral transformation of the data. In the bivariate case, the methodology is justified by the well known fact that an Archimedean copula is in one-to-one correspondence with its Kendall distribution. It turns out that this property continues to hold in the trivariate case, and strong evidence is provided that it extends to any dimension. A convenient criterion for the convergence of sequences of multivariate Archimedean copulas will be presented and used to show consistency of the proposed estimators.

Contact: johanna@math.mcgill.ca