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 …

In spatial statistics, fast and accurate parameter estimation, coupled with a reliable means of
uncertainty quantification, can be challenging when fitting a spatial process to real-world …

Regular vine sequences permit the organization of variables in a random vector along a
sequence of trees. Vine-based dependence models have become greatly popular as a way …

A Horse Race between the Block Maxima Method and the Peak-over-Threshold Approach
Page 1 Statistical Science 2021, Vol. 36, No. 3, 360–378 https://doi.org/10.1214/20-STS795 © …

We show how to perform full likelihood inference for max‐stable multivariate distributions or
processes based on a stochastic expectation–maximization algorithm, which combines …

This paper considers the specification of covariance structures with tail estimates. We focus
on two aspects:(i) the estimation of the VaR-CoVaR risk matrix in the case of larger number …

Statistical modeling of multivariate and spatial extreme events has attracted broad attention
in various areas of science. Max-stable distributions and processes are the natural class of …

The Supplementary Material (Lalancette, Engelke and Volgushev (2021)) is divided into six
sections. Section S1 contains the proofs of all main results, with a number of necessary …

We consider expected performances based on max-stable random fields and we are
interested in their derivatives with respect to the spatial dependence parameters of those …

We introduce a class of unbiased Monte Carlo estimators for multivariate densities of max-
stable fields generated by Gaussian processes. Our estimators take advantage of recent …