Rencontres Statistiques Lyonnaises

Bayesian inference for spatial extremes, with an application to extreme low temperatures

par Emeric THIBAUD (Colorado State University)

Europe/Paris
125 (ICJ)

125

ICJ

Bât. Braconnier
Description
Models for spatial extremes must account appropriately for asymptotic dependence, and this motivates the use of max-stable processes, which are the only non-trivial limits of properly rescaled pointwise maxima of random functions. The Brown-Resnick max-stable process has proven to be well-suited for modeling extremes of complex environmental processes, but in many applications its likelihood function is unobtainable and inference must be based on a composite likelihood, thereby preventing the use of classical Bayesian techniques. In this talk I will describe a new approach to full likelihood inference for max-stable processes, using componentwise maxima and their partitions in terms of individual events. This approach will be illustrated by the construction of a Bayesian hierarchical model for extreme low temperatures in northern Finland.