One- versus multi-component regular variation and extremes of Markov trees

DOI10.1017/apr.2020.22zbMath1473.60086arXiv1902.02226OpenAlexW3087838215MaRDI QIDQ5005037

Johan Segers

Publication date: 4 August 2021

Published in: Advances in Applied Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1902.02226

zbMATH Keywords

regular variationconditional independencePickands dependence functiongraphical modelHüsler-Reiss distributionmultivariate Pareto distributionmax-linear modelMarkov treetail measuretime change formulatail treeroot change formula

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Extreme value theory; extremal stochastic processes (60G70) Statistics of extreme values; tail inference (62G32)

Related Items (4)

Tail measures and regular variation ⋮ Total positivity in multivariate extremes ⋮ Extremes of Markov random fields on block graphs: max-stable limits and structured Hüsler-Reiss distributions ⋮ Inference on extremal dependence in the domain of attraction of a structured Hüsler-Reiss distribution motivated by a Markov tree with latent variables

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A continuous updating weighted least squares estimator of tail dependence in high dimensions
• Extremes on river networks
• Nonparametric estimation of multivariate extreme-value copulas
• Regularly varying measures on metric spaces: hidden regular variation and hidden jumps
• The generalized Pareto process; with a view towards application and simulation
• Transition kernels and the conditional extreme value model
• Conditioning on an extreme component: model consistency with regular variation on cones
• Regularly varying multivariate time series
• Extremal behaviour of stationary Markov chains with applications
• The tail process revisited
• Tail measure and spectral tail process of regularly varying time series
• Polar decomposition of regularly varying time series in star-shaped metric spaces
• Max-linear models on directed acyclic graphs
• Multivariate generalized Pareto distributions: parametrizations, representations, and properties
• Multivariate extreme value copulas with factor and tree dependence structures
• Functional regular variations, Pareto processes and peaks over threshold
• Maxima of normal random vectors: Between independence and complete dependence
• A sufficiency property arising from the characterization of extremes of Markov chains
• Spectral tail processes and max-stable approximations of multivariate regularly varying time series
• Limit laws for random vectors with an extreme component
• One-component regular variation and graphical modeling of extremes
• Regular variation for measures on metric spaces
• Graphical Models, Exponential Families, and Variational Inference
• The extremal index for a Markov chain
• Asymptotic Statistics
• Markov chain models for threshold exceedances
• Structure de dépendance des lois de valeurs extrêmes bivariées
• Markov tail chains
• Heavy-Tail Phenomena

This page was built for publication: One- versus multi-component regular variation and extremes of Markov trees