A class of high dimensional copulas based on products of bivariate copulas

Gildas Mazo (1) , Stéphane Girard (1) , Florence Forbes (1)

1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems

Copulas are a useful tool to model multivariate distributions. While there exist various families of bivariate copulas, much fewer has been done when the dimension is higher. To this aim an interesting class of copulas based on products of transformed copulas has been proposed. However the use of this class for practical high dimensional problems remains challenging. Constraints on the parameters and the product form render inference, and in particular the likelihood computation, difficult. In this paper we propose a new class of high dimensional copulas based on a product of transformed bivariate copulas. No constraints on the parameters refrain the applicability of the proposed class which is well suited for applications in high dimension. Furthermore the analytic forms of the copulas within this class allow to associate a natural graphical structure which helps to visualize the dependencies and to compute the likelihood efficiently even in high dimension.

Domaines: Méthodologie [stat.ME]

Gildas Mazo, Stéphane Girard, Florence Forbes. A class of high dimensional copulas based on products of bivariate copulas. 2013. ⟨hal-00910775v1⟩