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

K. 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 -