28 citations · 32 across the 8 of their papers we have counts for
6 papers · 1 filter
Spatial Aggregation of ROC and Precision-Recall Curves
Romain Pic, Zhongwei Zhang, Sebastian Engelke +1
Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are widely used to assess the discrimination ability of forecasts for binary events, such as threshold exce…
Extrapolation of extreme covariates in generalized additive regression using extreme-value theory
Viviana Carcaiso, Sebastian Engelke, Juliette Legrand +1
Predictions with covariates in the tails of the covariate distribution often lack accuracy and are highly uncertain but may be of critical importance in many applications. New cova…
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 ma…
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…
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…
Progression: an extrapolation principle for regression
Gloria Buriticá, Sebastian Engelke
The problem of regression extrapolation, or out-of-distribution generalization, arises when predictions are required at test points outside the range of the training data. In such…