123 citations · 267 across the 30 of their papers we have counts for
36 papers
Extremal Dependence of Moving Average Processes Driven by Exponential-Tailed Lévy Noise
Zhongwei Zhang, David Bolin, Sebastian Engelke +1
Moving average processes driven by exponential-tailed Lévy noise are important extensions of their Gaussian counterparts in order to capture deviations from Gaussianity, more flexi…
Asymmetric dependence in hydrological extremes
Cristina Deidda, Sebastian Engelke, Carlo De Michele
Extremal dependence describes the strength of correlation between the largest observations of two variables. It is usually measured with symmetric dependence coefficients that do n…
Graphical models for infinite measures with applications to extremes
Sebastian Engelke, Jevgenijs Ivanovs, Kirstin Strokorb
Conditional independence and graphical models are well studied for probability distributions on product spaces. We propose a new notion of conditional independence for any measure…
Statistical Inference for Hüsler-Reiss Graphical Models Through Matrix Completions
Manuel Hentschel, Sebastian Engelke, Johan Segers
The severity of multivariate extreme events is driven by the dependence between the largest marginal observations. The Hüsler-Reiss distribution is a versatile model for this extre…
Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk
Olivier C. Pasche, Sebastian Engelke
Risk assessment for extreme events requires accurate estimation of high quantiles that go beyond the range of historical observations. When the risk depends on the values of observ…
Modeling panels of extremes
Debbie J. Dupuis, Sebastian Engelke, Luca Trapin
Extreme value applications commonly employ regression techniques to capture cross-sectional heterogeneity or time-variation in the data. Estimation of the parameters of an extreme…
Extremal Random Forests
Nicola Gnecco, Edossa Merga Terefe, Sebastian Engelke
Classical methods for quantile regression fail in cases where the quantile of interest is extreme and only few or no training data points exceed it. Asymptotic results from extreme…
Total positivity in multivariate extremes
Frank Röttger, Sebastian Engelke, Piotr Zwiernik
Positive dependence is present in many real world data sets and has appealing stochastic properties that can be exploited in statistical modeling and in estimation. In particular,…
Learning extremal graphical structures in high dimensions
Sebastian Engelke, Michaël Lalancette, Stanislav Volgushev
Extremal graphical models encode the conditional independence structure of multivariate extremes. Key statistics for learning extremal graphical structures are empirical extremal v…
Modelling and simulating spatial extremes by combining extreme value theory with generative adversarial networks
Younes Boulaguiem, Jakob Zscheischler, Edoardo Vignotto +2
Modelling dependencies between climate extremes is important for climate risk assessment, for instance when allocating emergency management funds. In statistics, multivariate extre…
Estimation and Inference of Extremal Quantile Treatment Effects for Heavy-Tailed Distributions
David Deuber, Jinzhou Li, Sebastian Engelke +1
Causal inference for extreme events has many potential applications in fields such as climate science, medicine and economics. We study the extremal quantile treatment effect of a…
Distributionally robust tail bounds based on Wasserstein distance and -divergence
Corina Birghila, Maximilian Aigner, Sebastian Engelke
In this work, we provide robust bounds on the tail probabilities and the tail index of heavy-tailed distributions in the context of model misspecification. They are defined as the…
Gradient boosting for extreme quantile regression
Jasper Velthoen, Clément Dombry, Juan-Juan Cai +1
Extreme quantile regression provides estimates of conditional quantiles outside the range of the data. Classical quantile regression performs poorly in such cases since data in the…
Structure learning for extremal tree models
Sebastian Engelke, Stanislav Volgushev
Extremal graphical models are sparse statistical models for multivariate extreme events. The underlying graph encodes conditional independencies and enables a visual interpretation…
Rank-based Estimation under Asymptotic Dependence and Independence, with Applications to Spatial Extremes
Michaël Lalancette, Sebastian Engelke, Stanislav Volgushev
Multivariate extreme value theory is concerned with modeling the joint tail behavior of several random variables. Existing work mostly focuses on asymptotic dependence, where the p…
Sparse Structures for Multivariate Extremes
Sebastian Engelke, Jevgenijs Ivanovs
Extreme value statistics provides accurate estimates for the small occurrence probabilities of rare events. While theory and statistical tools for univariate extremes are well-deve…
Causal discovery in heavy-tailed models
Nicola Gnecco, Nicolai Meinshausen, Jonas Peters +1
Causal questions are omnipresent in many scientific problems. While much progress has been made in the analysis of causal relationships between random variables, these methods are…
Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction
Talia B. Kimber, Sebastian Engelke, Igor V. Tetko +2
In our study, we demonstrate the synergy effect between convolutional neural networks and the multiplicity of SMILES. The model we propose, the so-called Convolutional Neural Finge…
Graphical Models for Extremes
Sebastian Engelke, Adrien S. Hitz
Conditional independence, graphical models and sparsity are key notions for parsimonious statistical models and for understanding the structural relationships in the data. The theo…
Extreme Value Theory for Open Set Classification -- GPD and GEV Classifiers
Edoardo Vignotto, Sebastian Engelke
Classification tasks usually assume that all possible classes are present during the training phase. This is restrictive if the algorithm is used over a long time and possibly enco…
Extremal dependence of random scale constructions
Sebastian Engelke, Thomas Opitz, Jennifer Wadsworth
A bivariate random vector can exhibit either asymptotic independence or dependence between the largest values of its components. When used as a statistical model for risk assessmen…
Extremal Behavior of Aggregated Data with an Application to Downscaling
Sebastian Engelke, Raphael de Fondeville, Marco Oesting
The distribution of spatially aggregated data from a stochastic process may exhibit a different tail behavior than its marginal distributions. For a large class of aggregating…
Asymptotic properties of the maximum likelihood estimator for multivariate extreme value distributions
Clement Dombry, Sebastian Engelke, Marco Oesting
Max-stable distributions and processes are important models for extreme events and the assessment of tail risks. The full, multivariate likelihood of a parametric max-stable distri…
Statistical regionalization for estimation of extreme river discharges
Peiman Asadi, Sebastian Engelke, Anthony C. Davison
Regionalization methods have long been used to estimate high return levels of river discharges at ungauged locations on a river network. In these methods, the recorded discharge me…