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Estimation of extreme quantiles from heavy-tailed distributions in a location-dispersion regression model

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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Conference papers
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https://hal.inria.fr/hal-03040245
Contributor : Stephane Girard <>
Submitted on : Friday, December 4, 2020 - 11:26:44 AM
Last modification on : Monday, December 14, 2020 - 6:08:12 PM

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  • HAL Id : hal-03040245, version 1

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Stéphane Girard, Aboubacrène Ag Ahmad, El Hadji Deme, Aliou Diop, Antoine Usseglio-Carleve. Estimation of extreme quantiles from heavy-tailed distributions in a location-dispersion regression model. StressTest-2020 - International Workshop on Stress Test and Risk Management, Nov 2020, Paris / Virtual, France. ⟨hal-03040245⟩

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