A location-invariant probability weighted moment estimation of the Extreme Value Index
DOI10.1080/00207160.2014.975217zbMath1338.62148OpenAlexW2032733745MaRDI QIDQ2804923
Lígia Henriques-Rodrigues, Frederico Caeiro, M. Ivette Gomes
Publication date: 6 May 2016
Published in: International Journal of Computer Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207160.2014.975217
asymptotic propertiesMonte Carlo simulationheavy tailsextreme value indexstatistics of extremesadaptive semiparametric estimationbootstrap methodologylocation/scale-invariant estimation
Asymptotic distribution theory in statistics (62E20) Statistics of extreme values; tail inference (62G32) Monte Carlo methods (65C05)
Related Items (4)
Cites Work
- A simple generalisation of the Hill estimator
- Semi-parametric tail inference through probability-weighted moments
- Mixed moment estimator and location invariant alternatives
- Adaptive estimation of heavy right tails: resampling-based methods in action
- A simple general approach to inference about the tail of a distribution
- Optimal choice of sample fraction in extreme-value estimation
- The bootstrap methodology in statistics of extremes -- choice of optimal sample fraction
- ``Asymptotically unbiased estimators of the tail index based on external estimation of the second order parameter
- Semi-parametric probability-weighted moments estimation revisited
- Sur la distribution limite du terme maximum d'une série aléatoire
- Adaptive PORT–MVRB estimation: an empirical comparison of two heuristic algorithms
- PORT Hill and Moment Estimators for Heavy-Tailed Models
- Reduced-Bias Tail Index Estimators Under a Third-Order Framework
- Comparison of tail index estimators
- A simple second-order reduced bias’ tail index estimator
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