Tail Properties and Asymptotic Expansions for the Maximum of the Logarithmic Skew-Normal Distribution

DOI10.1239/JAP/1378401246zbMath1293.62036OpenAlexW2030109712MaRDI QIDQ2854090

Xin Liao, Zuo Xiang Peng, Saralees Nadarajah

Publication date: 17 October 2013

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

Full work available at URL: https://projecteuclid.org/euclid.jap/1378401246

zbMATH Keywords

maximumextreme value distributionpointwise convergence ratesubexponentialitylogarithmic skew-normal distribution

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Central limit and other weak theorems (60F05) Extreme value theory; extremal stochastic processes (60G70) Strong limit theorems (60F15)

Related Items (11)

Extremal properties of the skew-\(t\) distribution ⋮ Higher-order expansions of powered extremes of normal samples ⋮ Expansions for moments of logarithmic skew-normal extremes ⋮ Extremal properties of the beta-normal distribution ⋮ Tail behavior and extremes of the beta-normal distribution ⋮ Rates of convergence of extremes from skew-normal samples ⋮ On the norming constants for normal maxima ⋮ Asymptotic expansions of the moments of extremes from general error distribution ⋮ Higher-order expansions for distributions of extremes from general error distribution ⋮ Distributional expansion of maximum from logarithmic general error distribution ⋮ Asymptotic expansions of powered skew-normal extremes

Cites Work

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• Rates of convergence of extremes from skew-normal samples
• Skewed bivariate models and nonparametric estimation for the CTE risk measure
• An Introduction to Heavy-Tailed and Subexponential Distributions
• The Logarithmic Skew-Normal Distributions are Moment-Indeterminate
• Distributions that are both subexponential and in the domain of attraction of an extreme-value distribution

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