A Bayesian Hierarchical Spatial Copula Model: An Application to Extreme Temperatures in Extremadura (Spain)

A Bayesian hierarchical framework with a Gaussian copula and generalized extreme value (GEV) marginal distribution is proposed for the description of spatial dependencies in data. This model was applied to summer temperatures over Extremadura Region, southwest Spain, during period 1980–2015, compared noncopula model. The implemented Monte Carlo Markov Chain (MCMC) method that allows model’s parameters be estimated. results show GEV distribution’s shape parameter take constant negative values, location altitude dependent, scale values concentrated around same throughout region. Further, chosen presents lower deviance information criterion (DIC) when distributions are assumed than taken