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Pré-Publication, Document De Travail Année : 2009

Kernel estimators of extreme level curves

Résumé

We address the estimation of extreme level curves of heavy-tailed distributions. This problem is equivalent to estimating quantiles when covariate information is available and in the case where their order converges to one as the sample size increases. We show that, under some conditions, these so-called ``extreme conditional quantiles'' can still be estimated through a kernel estimator of the conditional survival function. Sufficient conditions on the rate of convergence of their order to one are provided to obtain asymptotically Gaussian distributed estimators. These results are illustrated both on simulated and real datasets.
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Dates et versions

inria-00393588 , version 1 (09-06-2009)
inria-00393588 , version 2 (20-11-2009)
inria-00393588 , version 3 (06-05-2010)
inria-00393588 , version 4 (23-04-2013)

Identifiants

  • HAL Id : inria-00393588 , version 1

Citer

Abdelaati Daouia, Laurent Gardes, Stéphane Girard, Alexandre Lekina. Kernel estimators of extreme level curves. 2009. ⟨inria-00393588v1⟩
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