B1179
Title: A Bayesian hierarchical spatial copula model
Authors: Mario M Pizarro - Universidad de Extremadura (Spain) [presenting]
M Isabel Parra Arevalo - Universidad de Extremadura (Spain)
Jose Agustin Garcia Garcia - University of Extremadura (Spain)
Francisco Javier Acero Diaz - University of Extremadura (Spain)
Abstract: A Bayesian hierarchical framework with a Gaussian copula and a generalized extreme value (GEV) marginal distribution is proposed for the description of spatial dependencies in data. The Bayesian hierarchical model was implemented with a Monte Carlo Markov Chain (MCMC) method that allows the distribution of the model's parameters to be estimated. The results show the GEV distribution's shape parameter to take constant negative values, the location parameter to be altitude dependent, and the scale parameter values to be concentrated around the same value throughout the space. Further, the spatial copula model chosen presents lower deviance information criterion (DIC) values when spatial distributions are assumed for the GEV distribution's location and scale parameters than when the scale parameter is taken to be constant over the whole space.
