A class of unbiased location invariant Hill-type estimators for heavy tailed distributions

DOI10.1214/08-EJS276zbMath1320.62111arXiv0809.3869OpenAlexW2035587974MaRDI QIDQ1951775

Jiaona Li, Zuo Xiang Peng, Saralees Nadarajah

Publication date: 24 May 2013

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0809.3869

zbMATH Keywords

asymptotic normalitysecond order regular variationlocation invariant Hill-type heavy tailed index estimator

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20) Statistics of extreme values; tail inference (62G32)

Related Items (7)

Location invariant heavy tail index estimation with block method ⋮ On an improvement of Hill and some other estimators ⋮ Asymptotic properties of generalized DPR statistic ⋮ Location invariant Weiss-Hill estimator ⋮ Asymptotic normality of location invariant heavy tail index estimator ⋮ A review of more than one hundred Pareto-tail index estimators ⋮ Strong convergence bound of the Pareto index estimator under right censoring

Cites Work

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• Asymptotically unbiased estimators for the extreme-value index
• Confidence regions for high quantiles of a heavy tailed distribution
• On the tail index of a heavy tailed distribution
• A moment estimator for the index of an extreme-value distribution
• Bootstrap and empirical likelihood methods in extremes
• Tail index estimation for heavy tails; accommodation of bias in the excesses over a high threshold
• A simple general approach to inference about the tail of a distribution
• ``Asymptotically unbiased estimators of the tail index based on external estimation of the second order parameter
• On exponential representations of log-spacings of extreme order statistics
• A class of asymptotically unbiased semi-parametric estimators of the tail index.
• Bias reduction and explicit semi-parametric estimation of the tail index
• A location invariant Hill-type estimator
• A NEW CALIBRATION METHOD OF CONSTRUCTING EMPIRICAL LIKELIHOOD-BASED CONFIDENCE INTERVALS FOR THE TAIL INDEX
• Tail Index Estimation for Heavy-Tailed Models: Accommodation of Bias in Weighted Log-Excesses

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