6 papers
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…
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…
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…
An asymptotic expansion of the empirical angular measure for bivariate extremal dependence
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…
Uniform concentration bounds for frequencies of rare events
Stéphane Lhaut, Anne Sabourin, Johan Segers
New Vapnik and Chervonenkis type concentration inequalities are derived for the empirical distribution of an independent random sample. Focus is on the maximal deviation over class…
Concentration bounds for the empirical angular measure with statistical learning applications
Stéphan Clémençon, Hamid Jalalzai, Stéphane Lhaut +2
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…