Expansions for Quantiles and Multivariate Moments of Extremes for Heavy Tailed Distributions

(...)The study of the asymptotes of the moments of Xn,r has been of considerable interest. McCord [12] gave a first approximation to the moments of Xn,1 for three classes. This showed that a moment of Xn,1 can behave like any positive power of n or n1 = log n. (Here, log is to the base e.) Pickands...

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Main Authors: Christopher Withers, Saralees Nadarajah
Format: Article
Language:English
Published: Instituto Nacional de Estatística | Statistics Portugal 2017-01-01
Series:Revstat Statistical Journal
Subjects:
Online Access:https://revstat.ine.pt/index.php/REVSTAT/article/view/202
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author Christopher Withers
Saralees Nadarajah
author_facet Christopher Withers
Saralees Nadarajah
author_sort Christopher Withers
collection DOAJ
description (...)The study of the asymptotes of the moments of Xn,r has been of considerable interest. McCord [12] gave a first approximation to the moments of Xn,1 for three classes. This showed that a moment of Xn,1 can behave like any positive power of n or n1 = log n. (Here, log is to the base e.) Pickands [15] explored the conditions under which various moments of (Xn,1 − bn) /an converge to the corresponding moments of the extreme value distribution. It was proved that this is indeed true for all F in the domain of attraction of an extreme value distribution provided that the moments are finite for sufficiently large n. Nair [13] investigated the limiting behavior of the distribution and the moments of Xn,1 for large n when F is the standard normal distribution function. (...)
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spelling doaj.art-41cda79584ad4a3f83add55f81b202cb2022-12-22T02:16:16ZengInstituto Nacional de Estatística | Statistics PortugalRevstat Statistical Journal1645-67262183-03712017-01-0115110.57805/revstat.v15i1.202Expansions for Quantiles and Multivariate Moments of Extremes for Heavy Tailed DistributionsChristopher Withers 0Saralees Nadarajah1Industrial Research Limited, Lower HuttUniversity of Manchester (...)The study of the asymptotes of the moments of Xn,r has been of considerable interest. McCord [12] gave a first approximation to the moments of Xn,1 for three classes. This showed that a moment of Xn,1 can behave like any positive power of n or n1 = log n. (Here, log is to the base e.) Pickands [15] explored the conditions under which various moments of (Xn,1 − bn) /an converge to the corresponding moments of the extreme value distribution. It was proved that this is indeed true for all F in the domain of attraction of an extreme value distribution provided that the moments are finite for sufficiently large n. Nair [13] investigated the limiting behavior of the distribution and the moments of Xn,1 for large n when F is the standard normal distribution function. (...) https://revstat.ine.pt/index.php/REVSTAT/article/view/202Bell polynomialsextremesinversion theoremmomentsquantiles
spellingShingle Christopher Withers
Saralees Nadarajah
Expansions for Quantiles and Multivariate Moments of Extremes for Heavy Tailed Distributions
Revstat Statistical Journal
Bell polynomials
extremes
inversion theorem
moments
quantiles
title Expansions for Quantiles and Multivariate Moments of Extremes for Heavy Tailed Distributions
title_full Expansions for Quantiles and Multivariate Moments of Extremes for Heavy Tailed Distributions
title_fullStr Expansions for Quantiles and Multivariate Moments of Extremes for Heavy Tailed Distributions
title_full_unstemmed Expansions for Quantiles and Multivariate Moments of Extremes for Heavy Tailed Distributions
title_short Expansions for Quantiles and Multivariate Moments of Extremes for Heavy Tailed Distributions
title_sort expansions for quantiles and multivariate moments of extremes for heavy tailed distributions
topic Bell polynomials
extremes
inversion theorem
moments
quantiles
url https://revstat.ine.pt/index.php/REVSTAT/article/view/202
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AT saraleesnadarajah expansionsforquantilesandmultivariatemomentsofextremesforheavytaileddistributions