How to make a Hill plot.
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Publication:1848777
DOI10.1214/aos/1016120372zbMath1106.62333OpenAlexW1999814784WikidataQ102129700 ScholiaQ102129700MaRDI QIDQ1848777
Holger Drees, Sidney I. Resnick, Laurens De Haan
Publication date: 14 November 2002
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.aos/1016120372
Nonparametric estimation (62G05) Order statistics; empirical distribution functions (62G30) Statistics of extreme values; tail inference (62G32)
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Cites Work
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- Laws of large numbers for sums of extreme values
- Approximate distributions of order statistics. With applications to nonparametric statistics
- Approximation of the Hill estimator process
- Tail index estimation for dependent data
- Optimal choice of sample fraction in extreme-value estimation
- Limit theory for bilinear processes with heavy-tailed noise
- Tail index estimation and an exponential regression model
- Selecting the optimal sample fraction in univariate extreme value estimation
- Smoothing the Hill Estimator
- Tail Index Estimation, Pareto Quantile Plots, and Regression Diagnostics
- Sample correlation behavior for the heavy tailed general bilinear process
- Consistency of Hill's estimator for dependent data
- Using a bootstrap method to choose the sample fraction in tail index estimation
- A bootstrap-based method to achieve optimality in estimating the extreme-value index
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