An extreme quantile estimator for the log-generalized Weibull-tail model - Archive ouverte HAL Access content directly
Journal Articles Econometrics and Statistics Year : 2020

An extreme quantile estimator for the log-generalized Weibull-tail model

(1) , (2) , (3) , (1, 4)
1
2
3
4

Abstract

A new estimator for extreme quantiles is proposed under the log-generalized Weibull-tail model, ntroduced by (de Valk, C., Extremes, pp. 661--686, vol. 19, 2016). This model relies on a new regular variation condition which, in some situations, permits to extrapolate further into the tails than the classical assumption in extreme-value theory. The asymptotic normality of the estimator is established and its finite sample properties are illustrated both on simulated and real datasets.
Fichier principal
Vignette du fichier
estimation-erv8.pdf (567.05 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01783929 , version 1 (02-05-2018)
hal-01783929 , version 2 (14-05-2018)
hal-01783929 , version 3 (21-09-2018)
hal-01783929 , version 4 (23-01-2019)

Identifiers

Cite

Clément Albert, Anne Dutfoy, Laurent Gardes, Stéphane Girard. An extreme quantile estimator for the log-generalized Weibull-tail model. Econometrics and Statistics , 2020, 13, pp.137-174. ⟨10.1016/j.ecosta.2019.01.004⟩. ⟨hal-01783929v4⟩
549 View
452 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More