6 papers
An Explicit Link between Extreme Value Theory and Compositional Data Analysis
Manuel Hentschel, Sebastian Engelke
Extreme value theory and compositional data analysis both study settings where relative information plays a central role. In multivariate extreme value theory, threshold exceedance…
Directional variograms for multivariate extremes
Manuel Hentschel, Frank Röttger, Johan Segers +1
Multivariate generalized Pareto distributions arise as limits of threshold exceedances and form a central model class for multivariate extremes. Existing inference methods based on…
Extrapolation in Statistical Learning with Extreme Value Theory
Sebastian Engelke, Nicola Gnecco, Anne Sabourin
Extreme value theory provides rigorous theory and statistical tools for extrapolation in machine learning, particularly in settings where traditional methods struggle due to data s…
Theoretical guarantees for neural estimators in parametric statistics
Almut Rödder, Manuel Hentschel, Sebastian Engelke
Neural estimators are simulation-based estimators for the parameters of a family of statistical models, which build a direct mapping from the sample to the parameter vector. They b…
Extremal graphical modeling with latent variables via convex optimization
Sebastian Engelke, Armeen Taeb
Extremal graphical models encode the conditional independence structure of multivariate extremes and provide a powerful tool for quantifying the risk of rare events. Prior work on…
Extremes of structural causal models
Sebastian Engelke, Nicola Gnecco, Frank Röttger
The behavior of extreme observations is well-understood for time series or spatial data, but little is known if the data generating process is a structural causal model (SCM). We s…