3 papers
stat.ML2026
Gradient boosting for extremes: sampling theory and application to insurance
Stéphane Lhaut, Olivier Lopez
We develop a statistical learning theory for gradient boosting applied to the estimation of covariate-dependent Generalized Pareto (GP) distributions in the context of Peaks-over-T…
stat.ML2026
Simulation of Multivariate Extremes: a Wasserstein-Aitchison GAN approach
Stéphane Lhaut, Holger Rootzén, Johan Segers
Economically responsible mitigation of multivariate extreme risks-such as extreme rainfall over large areas, large simultaneous variations in many stock prices, or widespread break…
math.ST2024
Testing parametric models for the angular measure for bivariate extremes
Stéphane Lhaut, Johan Segers
The angular measure on the unit sphere characterizes the first-order dependence structure of the components of a random vector in extreme regions and is defined in terms of standar…