# Extreme value distributions for dependent jointly ln,p-symmetrically distributed random variables

Dependence Modeling (2016)

- Volume: 4, Issue: 1, page 30-62, electronic only
- ISSN: 2300-2298

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topK. Müller, and W.-D. Richter. "Extreme value distributions for dependent jointly ln,p-symmetrically distributed random variables." Dependence Modeling 4.1 (2016): 30-62, electronic only. <http://eudml.org/doc/276572>.

@article{K2016,

abstract = {A measure-of-cone representation of skewed continuous ln,p-symmetric distributions, n ∈ N, p > 0, is proved following the geometric approach known for elliptically contoured distributions. On this basis, distributions of extreme values of n dependent random variables are derived if the latter follow a joint continuous ln,p-symmetric distribution. Light, heavy, and extremely far tails as well as tail indices are discussed, and new parameters of multivariate tail behavior are introduced.},

author = {K. Müller, W.-D. Richter},

journal = {Dependence Modeling},

keywords = {measure-of-cone representation; p-generalized Laplace and Gaussian distributions; skewed ln,psymmetric
distribution; tail index, light/ heavy center of distribution; $p$-generalized Laplace and Gaussian distributions; skewed $l_\{n,p\}$-symmetric distribution},

language = {eng},

number = {1},

pages = {30-62, electronic only},

title = {Extreme value distributions for dependent jointly ln,p-symmetrically distributed random variables},

url = {http://eudml.org/doc/276572},

volume = {4},

year = {2016},

}

TY - JOUR

AU - K. Müller

AU - W.-D. Richter

TI - Extreme value distributions for dependent jointly ln,p-symmetrically distributed random variables

JO - Dependence Modeling

PY - 2016

VL - 4

IS - 1

SP - 30

EP - 62, electronic only

AB - A measure-of-cone representation of skewed continuous ln,p-symmetric distributions, n ∈ N, p > 0, is proved following the geometric approach known for elliptically contoured distributions. On this basis, distributions of extreme values of n dependent random variables are derived if the latter follow a joint continuous ln,p-symmetric distribution. Light, heavy, and extremely far tails as well as tail indices are discussed, and new parameters of multivariate tail behavior are introduced.

LA - eng

KW - measure-of-cone representation; p-generalized Laplace and Gaussian distributions; skewed ln,psymmetric
distribution; tail index, light/ heavy center of distribution; $p$-generalized Laplace and Gaussian distributions; skewed $l_{n,p}$-symmetric distribution

UR - http://eudml.org/doc/276572

ER -

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