Climate and weather variables such as rainfall, temperature, and pressure are indicators for
hazards such as tropical cyclones, floods, and fires. The impact of these events can be due …

Research in the statistical analysis of extreme values has flourished over the past decade:
new probability models, inference and data analysis techniques have been introduced; and …

Multivariate extreme value theory and methods concern the characterization, estimation and
extrapolation of the joint tail of the distribution of ad-dimensional random variable. Existing …

Probability distributions of multivariate random variables are generally more complex
compared to their univariate counterparts which is due to a possible nonlinear dependence …

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

Current dependence models for spatial extremes are based upon max-stable processes.
Within this class, there are few inferentially viable models available, and we propose one …

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

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 …

Extreme value methodology is being increasingly used by practitioners from a wide range of
fields. The importance of accurately modeling extreme events has intensified, particularly in …

Different dependence scenarios can arise in multivariate extremes, entailing careful
selection of an appropriate class of models. In bivariate extremes, the variables are either …