The "Dependence models and copulas (DMC)" team is concerned with all methodological and computational aspects of dependence models and copulas. Nowadays, an important task of multivariate statistics is the determination of a meaningful description of the relationships between different random variables that can be used in order to describe the phenomena of interest. A convenient way to do this consists of adopting a copula-approach that is based on a two-step procedure: first, the marginal distributions are fixed; then, the copula is chosen in order to describe the (rank-invariant dependence) among the considered variables. Copula-based models offer a convenient alternative to standard (linear, Gaussian) models and, due to their flexibility, have proved useful in a variety of domains. Applications range from ''classical'' areas in which copulas have already been adopted successfully (finance, insurance, economics, marketing, etc.) to biological and environmental sciences (climate change, meteorology, hydrology, etc.).
Issues related to the team include fundamental aspects (especially from a mathematical point of view) as well as methodological aspects (inference for copulas, copula-based empirical processes, etc.). Additionally, computational aspects, in particular in relation with high-dimensional and asymmetric models, will be of interest and the development of specific software (preferably in an open source environment like R) is highly welcome.