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B0237
Title: Bayesian copula modelling in the presence of covariates Authors:  Julian Stander - Plymouth University (United Kingdom) [presenting]
Luciana Dalla Valle - University of Plymouth (United Kingdom)
Charlotte Taglioni - University of Plymouth (United Kingdom)
Mario Cortina Borja - Institute of Child Health (United Kingdom)
Abstract: Copula models separate the dependence structure in a multivariate distribution from its univariate marginals, so overcoming many of the issues associated with commonly used statistical modelling methods by allowing, for example, different complex asymmetric dependencies and tail behaviours to be modelled. We discuss the modelling of bivariate data using copulas, of which there are now a rich choice. The parameter or parameters of the copula density are modelled as a function of a covariate using a natural cubic spline. Working in the Bayesian framework, we perform inference on the natural cubic spline and an associated smoothing parameter. We also discuss the choice of the copula density itself. We illustrate our approach using data from finance and medicine. We outline the extension of our methodology to more than one covariate and to multivariate data.