9 papers
Extrapolation of extreme covariates in generalized additive regression using extreme-value theory
Viviana Carcaiso, Sebastian Engelke, Juliette Legrand +1
We propose methods to enhance the predictive performance of generalized additive models (GAMs) in the context of covariate extrapolation, where predictions rely on covariates beyon…
Extreme Conformal Prediction: Reliable Intervals for High-Impact Events
Olivier C. Pasche, Henry Lam, Sebastian Engelke
Conformal prediction is a popular method to construct prediction intervals with marginal coverage guarantees from black-box machine learning models. In applications with potentiall…
A Kullback-Leibler divergence test for multivariate extremes: theory and practice
Sebastian Engelke, Philippe Naveau, Chen Zhou
Testing whether two multivariate samples exhibit the same extremal behavior is an important problem in various fields including environmental and climate sciences. While several ad…
Graph structure learning for stable processes
Florian Brück, Sebastian Engelke, Stanislav Volgushev
We introduce Ising-Hüsler-Reiss processes, a new class of multivariate Lévy processes that allows for sparse modeling of the path-wise conditional independence structure between…
Intrinsic Whittle--Matérn fields and sparse spatial extremes
David Bolin, Peter Braunsteins, Sebastian Engelke +1
Intrinsic Gaussian fields are used in many areas of statistics as models for spatial or spatio-temporal dependence, or as priors for latent variables. However, there are two major…
Boosted Control Functions: Distribution generalization and invariance in confounded models
Nicola Gnecco, Jonas Peters, Sebastian Engelke +1
Modern machine learning methods and the availability of large-scale data have significantly advanced our ability to predict target quantities from large sets of covariates. However…