The classical modeling of spatial extremes relies on asymptotic models (ie, max‐stable or r‐
Pareto processes) for block maxima or peaks over high thresholds, respectively. However, at …

Statistics of extremes concerns inference for rare events. Often the events have never yet
been observed, and their probabilities must therefore be estimated by extrapolation of tail …

The health consequences of climate variability and change are diverse, potentially affecting
the burden of a wide range of health outcomes, including illnesses and deaths related to …

Max-stable processes are increasingly widely used for modelling complex extreme events,
but existing fitting methods are computationally demanding, limiting applications to a few …

Currently available models for spatial extremes suffer either from inflexibility in the
dependence structures that they can capture, lack of scalability to high dimensions, or in …

Recent advances in extreme value theory have established-Pareto processes as the natural
limits for extreme events defined in terms of exceedances of a risk functional. In this paper …

Mitigating the risk arising from extreme events is a fundamental goal with many applications,
such as the modelling of natural disasters, financial crashes, epidemics, and many others …

Spatial extreme value analysis has been an area of rapid growth in the last decade. The
focus has been on modelling the spatial componentwise maxima by max-stable processes …

Different dependence scenarios can arise in multivariate extremes, entailing careful
selection of an appropriate class of models. In bivariate extremes, the variables are either …

Various natural phenomena exhibit spatial extremal dependence at short spatial distances.
However, existing models proposed in the spatial extremes literature often assume that …