Title: Full likelihood inference for max-stable data,
Abstract: We show how to perform full likelihood inference for max-stable multivariate
distributions or processes based on a stochastic Expectation-Maximisation
algorithm, which combines statistical and computational efficiency in
high-dimensions. The good performance of this methodology is demonstrated by
simulation based on the popular logistic and Brown--Resnick models, and it is
shown to provide dramatic computational time improvements with respect to a
direct computation of the likelihood. Strategies to further reduce the
computational burden are also discussed.