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 …

We present a new method for linear and nonlinear, lagged and contemporaneous constraint-
based causal discovery from observational time series in the presence of latent …

The Supplementary Material contains additional information on Algorithms 1 and 2, the
simulation study on independent data, additional results for the simulation study on …

Probabilistic modelling and statistical analysis of environmental conditions is important for
the design and assessment of ships and other marine structures. It will give a necessary …

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 …

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 …

The supplementary material (Gnecco et al.(2020)) consists of six sections. Section A
summarises important facts about regularly varying random variables. Section B contains …

Modeling dependencies between climate extremes is important for climate risk assessment,
for instance when allocating emergency management funds. In statistics, multivariate …

Extremal graphical models are sparse statistical models for multivariate extreme events. The
underlying graph encodes conditional independencies and enables a visual interpretation …