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 …

Extreme value statistics provides accurate estimates for the small occurrence probabilities of
rare events. While theory and statistical tools for univariate extremes are well developed …

Conditional independence, graphical models and sparsity are key notions for parsimonious
statistical models and for understanding the structural relationships in the data. The theory of …

Many environmental processes exhibit weakening spatial dependence as events become
more extreme. Well-known limiting models, such as max-stable or generalized Pareto …

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 …

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 …

Changes in the Earth's climate have been increasingly observed. Assessing the likelihood
that each of these changes has been caused by human influence is important for decision …

The severity of multivariate extreme events is driven by the dependence between the largest
marginal observations. The Hüsler–Reiss distribution is a versatile model for this extremal …

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 …

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