Grid-Uniform Copulas and Rectangle Exchanges: Bayesian Model and Inference for a Rich Class of Copula Functions

Copula-based models provide a great deal of flexibility in modelling multivariate distributions, allowing for the specifications marginal distributions separately from dependence structure (copula) that links them to form joint distribution. Choosing class copula is not trivial task and its misspecification can lead wrong conclusions. We introduce novel grid-uniform functions, which dense space all continuous functions Hellinger sense. propose Bayesian model based on this posterior distribution strongly consistent develop an automatic Markov chain Monte Carlo algorithm exploring corresponding The methodology illustrated by means simulated data compared main existing approach.