Max-stable processes are natural models for spatial extremes because they provide suitable
asymptotic approximations to the distribution of maxima of random fields. In the recent past …

The last decade has seen max-stable processes emerge as a common tool for the statistical
modeling of spatial extremes. However, their application is complicated due to the …

Spatial extreme value analysis is useful to environmental studies, in which extreme value
phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable …

Max-stable processes play a fundamental role in modeling the spatial dependence of
extremes because they appear as a natural extension of multivariate extreme value …

Statistical analysis of max-stable processes used to model spatial extremes has been limited
by the difficulty in calculating the joint likelihood function. This precludes all standard …

Extreme environmental phenomena such as major precipitation events manifestly exhibit
spatial dependence. Max-stable processes are a class of asymptotically-justified models that …

We propose a hierarchical modeling approach for explaining a collection of point-referenced
extreme values. In particular, annual maxima over space and time are assumed to follow …

Parametric inference for spatial max-stable processes is difficult since the related likelihoods
are unavailable. A composite likelihood approach based on the bivariate distribution of …

This chapter aims at being a crash course on max-stable processes with an emphasis on
their use for modeling spatial extremes. We will see how max-stable processes are defined …

Recently there has been a lot of effort to model extremes of spatially dependent data. These
efforts seem to be divided into two distinct groups: the study of max-stable processes …