Estimation of extreme quantiles from heavy and light tailed distributions

DOI10.1016/J.JSPI.2012.03.025zbMath1428.62199OpenAlexW2071417641MaRDI QIDQ449352

Jonathan El Methni, Stéphane Girard, Laurent Gardes, Armelle Guillou

Publication date: 30 August 2012

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2012.03.025

zbMATH Keywords

asymptotic normalitymaximum domain of attractionextreme quantilePareto-type distributionsWeibull tail-distributions

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Order statistics; empirical distribution functions (62G30) Statistics of extreme values; tail inference (62G32)

Related Items (9)

Nonparametric estimation of the conditional tail index and extreme quantiles under random censoring ⋮ On dealing with the unknown population minimum in parametric inference ⋮ Asymptotic behavior of the extrapolation error associated with the estimation of extreme quantiles ⋮ A refined Weissman estimator for extreme quantiles ⋮ Estimation of conditional extreme risk measures from heavy-tailed elliptical random vectors ⋮ Estimation of extreme quantiles from heavy and light tailed distributions ⋮ Nonparametric adaptive estimation of conditional probabilities of rare events and extreme quantiles ⋮ Quantile predictions for elliptical random fields ⋮ Estimation of extremes for heavy-tailed and light-tailed distributions in the presence of random censoring

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