Wednesday 02 Dec 2015Bayesian Copula Modelling in the Presence of Covariates

Julian Stander - Plymouth University

RLB209, Plymouth University 14:00-15:00

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, di erent 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 child health and nance. We mention the extension of our methodology to more than one covariate and to multivariate data.

Please note that this talk will last considerably less than one hour. There may be the possibility also to discuss briefly a fun application of survival analysis to historical data that yields interesting insights about how life expectancies may have changed since 1400, but this is not guaranteed.

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