Optimal rates of convergence in the Weibull model based on kernel-type estimators
Résumé
Let $F$ be a distribution function in the maximal domain of attraction of the Gumbel distribution and such that $-\log(1-F(x)) = x^{1/\theta} L(x)$ for a positive real number $\theta$, called the Weibul tail index, and a slowly varying function~$L$. It is well known that the estimators of $\theta$ have a very slow rate of convergence. We establish here a sharp optimality result in the minimax sense, that is when $L$ is treated as an infinite dimensional nuisance parameter belonging to some functional class. We also establish the rate optimal asymptotic property of a data-driven choice of the sample fraction that is used for estimation.
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