Bayesian inference for multivariate extreme value distributions
Statistical modeling of multivariate and spatial extreme events has attracted broad attention in various areas of science. Max stable distributions and processes are the natural class of models for this purpose, and many parametric families have been developed and successfully applied. Due to complicated likelihoods, the efficient statistical inference is still an active area of research, and usually composite likelihood methods based on bivariate densities only are used. Thibaud et al. [2016, \emph{Ann.~Appl.~Stat. to appear] use a Bayesian approach to fit a Brown Resnick process to extreme temperatures. In this paper we extend this idea to a methodology that is applicable to general max stable distributions and that uses full likelihoods. In a frequentist framework, a simulation study shows that the median of the posterior distribution is considerably more efficient than the composite likelihood estimator. From a Bayesian perspective, our approach opens the way for new techniques such as Bayesian model comparison and prediction in multivariate and spatial extremes.