Smooth tail-index estimation

DOI10.1080/00949650802142667zbMath1179.62075arXivmath/0612140OpenAlexW2071893706WikidataQ61810972 ScholiaQ61810972MaRDI QIDQ3401368

Samuel Müller, Kaspar Rufibach

Publication date: 29 January 2010

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/math/0612140

zbMATH Keywords

extreme-value theoryPickands estimatortail-index estimationnegative Hill estimatorlog-concave density estimationsmall-sample performance

Mathematics Subject Classification ID

Density estimation (62G07) Order statistics; empirical distribution functions (62G30) Statistics of extreme values; tail inference (62G32) Monte Carlo methods (65C05)

Related Items (13)

Small-sample one-sided testing in extreme value regression models ⋮ Maximum likelihood estimation of a log-concave density and its distribution function: basic properties and uniform consistency ⋮ Quasi-concave density estimation ⋮ On robust tail index estimation ⋮ Inference for Local Parameters in Convexity Constrained Models ⋮ Inference and modeling with log-concave distributions ⋮ Recent progress in log-concave density estimation ⋮ On the max-domain of attraction of distributions with log-concave densities ⋮ A review of more than one hundred Pareto-tail index estimators ⋮ Partially smooth tail-index estimation for small samples ⋮ A Bayesian nonparametric approach to log-concave density estimation ⋮ Small-sample likelihood inference in extreme-value regression models ⋮ Limit distribution theory for maximum likelihood estimation of a log-concave density

Uses Software

• logcondens
• smoothtail

Cites Work

• Generalized Pickands estimators for the extreme value index
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• Maximum likelihood estimation in a class of nonregular cases
• On the maximal life span of humans
• On optimising the estimation of high quantiles of a probability distribution
• Computing maximum likelihood estimators of a log-concave density function

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