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2020 Estimation of extreme quantiles from heavy-tailed distributions in a location-dispersion regression model
Aboubacrène Ag Ahmad, El Hadji Deme, Aliou Diop, Stéphane Girard, Antoine Usseglio-Carleve
Electron. J. Statist. 14(2): 4421-4456 (2020). DOI: 10.1214/20-EJS1779

Abstract

We consider a location-dispersion regression model for heavy-tailed distributions when the multidimensional covariate is deterministic. In a first step, nonparametric estimators of the regression and dispersion functions are introduced. This permits, in a second step, to derive an estimator of the conditional extreme-value index computed on the residuals. Finally, a plug-in estimator of extreme conditional quantiles is built using these two preliminary steps. It is shown that the resulting semi-parametric estimator is asymptotically Gaussian and may benefit from the same rate of convergence as in the unconditional situation. Its finite sample properties are illustrated both on simulated and real tsunami data.

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Aboubacrène Ag Ahmad. El Hadji Deme. Aliou Diop. Stéphane Girard. Antoine Usseglio-Carleve. "Estimation of extreme quantiles from heavy-tailed distributions in a location-dispersion regression model." Electron. J. Statist. 14 (2) 4421 - 4456, 2020. https://doi.org/10.1214/20-EJS1779

Information

Received: 1 September 2020; Published: 2020
First available in Project Euclid: 31 December 2020

MathSciNet: MR4194267
Digital Object Identifier: 10.1214/20-EJS1779

Subjects:
Primary: 62G32
Secondary: 62E20, 62G30

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Vol.14 • No. 2 • 2020
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