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 …

This review paper surveys recent development in software implementations for extreme
value analyses since the publication of Stephenson and Gilleland (Extremes 8: 87–109,) …

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

We investigate the changing nature of the frequency, magnitude, and spatial extent of
extreme temperatures in Ireland from 1942 to 2020. We develop an extreme value model …

Environmental data science for spatial extremes has traditionally relied heavily on max-
stable processes. Even though the popularity of these models has perhaps peaked with …

From environmental sciences to finance, there are growing needs for assessing the risk of
more extreme events than those observed. Extrapolating extreme events beyond the range …

We introduce an approach to quickly and accurately approximate the cumulative distribution
function of multivariate Gaussian distributions arising from spatial Gaussian processes. This …

Climate change has resulted in extreme events becoming more frequent, intense, and
destructive. It is essential to understand the behaviour of extreme weather processes for …

Chapter 1: We analyze the joint tail of two variables related to fire threat associated with
SantaAna Winds in Southern California. To do this, we apply a flexible model for the joint tail …