Optimal rates of convergence in the Weibull model based on kernel-type estimators

DOI10.1016/j.spl.2011.11.022zbMath1237.62056OpenAlexW1987431858MaRDI QIDQ419172

Philippe Soulier, Cécile Mercadier

Publication date: 18 May 2012

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2011.11.022

zbMATH Keywords

sequential procedureoptimal sample fractionWeibull tail index

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Minimax procedures in statistical decision theory (62C20) Statistics of extreme values; tail inference (62G32)

Cites Work

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• Pitfalls in using Weibull tailed distributions
• Kernel estimates of the tail index of a distribution
• Weighted empirical and quantile processes
• Selecting the optimal sample fraction in univariate extreme value estimation
• Optimal rates of convergence for estimates of the extreme value index
• The asymptotic distribution of weighted empirical distribution functions
• Semiparametric lower bounds for tail index estimation
• Estimation of distribution tails —a semiparametric approach
• A Hill Type Estimator of the Weibull Tail-Coefficient
• Sharp Optimality in Density Deconvolution with Dominating Bias. I
• Introduction to nonparametric estimation

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