Statistical inference for Hüsler–Reiss graphical models through matrix completions
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 extremal …
marginal observations. The Hüsler–Reiss distribution is a versatile model for this extremal …
Graphical models for multivariate extremes
S Engelke, M Hentschel, M Lalancette… - arXiv preprint arXiv …, 2024 - arxiv.org
Graphical models in extremes have emerged as a diverse and quickly expanding research
area in extremal dependence modeling. They allow for parsimonious statistical methodology …
area in extremal dependence modeling. They allow for parsimonious statistical methodology …
Extremal graphical modeling with latent variables
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 and provide a powerful tool for quantifying the risk of rare events. Prior work on …
Modeling Extreme Events: Univariate and Multivariate Data-Driven Approaches
Modern inference in extreme value theory faces numerous complications, such as missing
data, hidden covariates or design problems. Some of those complications were exemplified …
data, hidden covariates or design problems. Some of those complications were exemplified …
Principal component analysis for max-stable distributions
F Reinbott, A Janßen - arXiv preprint arXiv:2408.10650, 2024 - arxiv.org
Principal component analysis (PCA) is one of the most popular dimension reduction
techniques in statistics and is especially powerful when a multivariate distribution is …
techniques in statistics and is especially powerful when a multivariate distribution is …
High dimensional inference for extreme value indices
L Chen, C Zhou - arXiv preprint arXiv:2407.20491, 2024 - arxiv.org
When applying multivariate extreme values statistics to analyze tail risk in compound events
defined by a multivariate random vector, one often assumes that all dimensions share the …
defined by a multivariate random vector, one often assumes that all dimensions share the …
Characterizing extremal dependence on a hyperplane
P Wan - arXiv preprint arXiv:2411.00573, 2024 - arxiv.org
Quantifying the risks of extreme scenarios requires understanding the tail behaviours of
variables of interest. While the tails of individual variables can be characterized …
variables of interest. While the tails of individual variables can be characterized …
[PDF][PDF] Hüsler–Reiss Graphical Models for Multivariate Extremes
M Hentschel - 2024 - gmc.sciencesconf.org
Hüsler–Reiss Graphical Models for Multivariate Extremes Page 1 Hüsler–Reiss Graphical
Models for Multivariate Extremes Manuel Hentschel University of Geneva May 16, 2024 …
Models for Multivariate Extremes Manuel Hentschel University of Geneva May 16, 2024 …