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 …

Several studies have advocated the use of extreme value theory (EVT) traffic conflict models
for real-time crash risk prediction using real-time safety indices such as the risk of crash (RC) …

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 …

Max-stable processes are the natural analogues of the generalized extreme value
distribution when modelling extreme events in space and time. Under suitable conditions …

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

This book presents a broad overview of statistical modeling of extreme events along with the
most recent methodologies and various applications. It brings together background material …

Gaussian scale mixtures are constructed as Gaussian processes with a random variance.
They have non-Gaussian marginals and can exhibit asymptotic dependence unlike …

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 …