Characterization of multivariate heavy-tailed distribution families via copula

DOI10.1016/j.jmva.2011.12.001zbMath1236.62048OpenAlexW3123856454MaRDI QIDQ765839

Yi Zhang, Chengguo Weng

Publication date: 22 March 2012

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2011.12.001

zbMATH Keywords

multivariate regular variationtail dependence functionmultivariate long-tailed distributionsmultivariate subexponential distributions

Mathematics Subject Classification ID

Characterization and structure theory for multivariate probability distributions; copulas (62H05) Statistics of extreme values; tail inference (62G32)

Related Items (6)

Testing for Positive Quadrant Dependence ⋮ PATHS AND INDICES OF MAXIMAL TAIL DEPENDENCE ⋮ On \(1/f\) noise ⋮ Regular variation, conditions of domain of attraction and the existence of the tail dependence function in the general dependence case: a copula approach ⋮ Extremes for multivariate expectiles ⋮ On multivariate extensions of the conditional value-at-risk measure

Cites Work

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• An introduction to copulas.
• Multivariate Archimedean copulas, \(d\)-monotone functions and \(\ell _{1}\)-norm symmetric distributions
• Asymptotic results for the sum of dependent non-identically distributed random variables
• Tails of multivariate Archimedean copulas
• Multivariate subexponential distributions
• Subexponentiality of the product of independent random variables
• Extremal behavior of Archimedean copulas
• Tail Dependence for Heavy-Tailed Scale Mixtures of Multivariate Distributions
• Limit theory for multivariate sample extremes
• Asymptotic independence and a network traffic model
• Heavy-Tail Phenomena

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