The areal modeling of the extremes of a natural process such as rainfall or temperature is
important in environmental statistics; for example, understanding extreme areal rainfall is …

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 …

Supplement to “Extremes on river networks”. The supplementary material contains the
following: a PDF document containing the derivation of the new likelihood representation …

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

Max-stable processes play an important role as models for spatial extreme events. Their
complex structure as the pointwise maximum over an infinite number of random functions …

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 …

Estimation of extreme value parameters from observations in the max-domain of attraction of
a multivariate max-stable distribution commonly uses aggregated data such as block …

Spatial modeling of rare events has obvious applications in the environmental sciences and
is crucial when assessing the effects of catastrophic events (such as heatwaves or …

Max-stable processes arise as the only possible nontrivial limits for maxima of affinely
normalized identically distributed stochastic processes, and thus form an important class of …