Kernel-type estimators for the extreme value index

DOI10.1214/aos/1074290333zbMath1047.62046OpenAlexW2058786979MaRDI QIDQ1430919

P. P. de Wolf, Hendrik P. Lopuhaä, Piet Groeneboom

Publication date: 27 May 2004

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1074290333

zbMATH Keywords

adaptive estimationextreme value indexsecond order parameter estimation

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Statistics of extreme values; tail inference (62G32)

Related Items (18)

A new partially reduced-bias mean-of-order \(p\) class of extreme value index estimators ⋮ Estimation of the extreme-value index and generalized quantile plots ⋮ Kernel estimation of the tail index of a right-truncated Pareto-type distribution ⋮ A supermartingale argument for characterizing the functional Hill process weak law for small parameters ⋮ Extreme Value Theory and Statistics of Univariate Extremes: A Review ⋮ Multivariate Hill Estimators ⋮ Bias reduction in kernel tail index estimation for randomly truncated Pareto-type data ⋮ A weighted composite log-likelihood approach to parametric estimation of the extreme quantiles of a distribution ⋮ Reduced-bias estimator of the Proportional Hazard Premium for heavy-tailed distributions ⋮ Uniform in bandwidth consistency of kernel estimators of the tail index ⋮ A review of more than one hundred Pareto-tail index estimators ⋮ Partially smooth tail-index estimation for small samples ⋮ Statistics of extremes for IID data and breakthroughs in the estimation of the extreme value index: Laurens de Haan leading contributions ⋮ Bootstrap and empirical likelihood methods in extremes ⋮ Nonparametric estimation for a class of Lévy processes ⋮ Smooth tail-index estimation ⋮ The estimation for Lévy processes in high frequency data ⋮ A functional law of the iterated logarithm for kernel-type estimators of the tail index

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