A class of location invariant estimators for heavy tailed distributions
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Publication:5875268
DOI10.1080/03610926.2021.1931335OpenAlexW3176889865MaRDI QIDQ5875268
Publication date: 3 February 2023
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2021.1931335
Asymptotic properties of parametric estimators (62F12) Extreme value theory; extremal stochastic processes (60G70) Statistics of extreme values; tail inference (62G32)
Cites Work
- A simple generalisation of the Hill estimator
- On an improvement of Hill and some other estimators
- Mixed moment estimator and location invariant alternatives
- Tail estimates motivated by extreme value theory
- A moment estimator for the index of an extreme-value distribution
- Kernel estimates of the tail index of a distribution
- On asymptotic normality of Hill's estimator for the exponent of regular variation
- On estimating the endpoint of a distribution
- Laws of large numbers for sums of extreme values
- Statistical inference using extreme order statistics
- A simple general approach to inference about the tail of a distribution
- Excess functions and estimation of the extreme-value index
- A location invariant Hill-type estimator
- The harmonic moment tail index estimator: asymptotic distribution and robustness
- Some Best Parameter Estimates for Distributions with Finite Endpoint
- PORT Hill and Moment Estimators for Heavy-Tailed Models
- Tail Index Estimation for Heavy-Tailed Models: Accommodation of Bias in Weighted Log-Excesses
- Central limit theorems for sums of extreme values
- SLOW VARIATION WITH REMAINDER: THEORY AND APPLICATIONS
- Almost sure convergence of the Hill estimator
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