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Pré-Publication, Document De Travail Année : 2020

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

Résumé

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 precision 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 benefits from the same rate of convergence as in the unconditional situation. Its finite sample properties are illustrated both on simulated and real data.
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Dates et versions

hal-02486937 , version 1 (21-02-2020)
hal-02486937 , version 2 (11-03-2020)
hal-02486937 , version 3 (16-09-2020)

Identifiants

  • HAL Id : hal-02486937 , version 1

Citer

Aboubacrène Ag, Hadji Deme, Aliou Diop, Stéphane Girard, Antoine Usseglio-Carleve. Estimation of extreme quantiles of heavy-tailed distributions in a location-dispersion regression model. 2020. ⟨hal-02486937v1⟩
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