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Title: Grid-uniform copulas and rectangle exchanges: Model and Bayesian inference for a rich class of copula functions Authors:  Nicolas Kuschinski - Pontificia Universidad Católica de Chile (Chile) [presenting]
Alejandro Jara - Pontificia Universidad Católica de Chile (Chile)
Abstract: Copula-based models provide a great deal of flexibility in modelling multivariate distributions, allowing for the specifications of models for the marginal distributions separately from the dependence structure (copula) that links them to form a joint distribution. Choosing a class of copula models is not a trivial task, but it can be simplified by relying on rich classes of copula functions. We introduce a novel class of grid-uniform copula functions, which is dense (in the Hellinger sense) in the space of all continuous copula functions. We propose a Bayesian model based on this class and develop an efficient Markov chain Monte Carlo algorithm for exploring the corresponding posterior distribution in arbitrarily many dimensions, allowing for the semiparametric or nonparametric modelling of continuous joint distributions.