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 …

Changes in the Earth's climate have been increasingly observed. Assessing the likelihood
that each of these changes has been caused by human influence is important for decision …

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 …

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 …

Climate anomalies pose risks to agriculture and food security. To assess the impact, this
paper models the complex dependences of climate extreme indices and the crop-related …

Classical methods for quantile regression fail in cases where the quantile of interest is
extreme and only few or no training data points exceed it. Asymptotic results from extreme …

Modelling extremes of spatial aggregates of precipitation using conditional methods Page 1
The Annals of Applied Statistics 2022, Vol. 16, No. 4, 2693–2713 https://doi.org/10.1214/22-AOAS1609 …

Peaks-over-threshold analysis using the generalised Pareto distribution is widely applied in
modelling tails of univariate random variables, but much information may be lost when …

Extremal graphical models encode the conditional independence structure of multivariate
extremes. For the popular class of H\" usler--Reiss models, we propose a majority voting …