Tail index estimation with a fixed tuning parameter fraction

DOI10.1016/j.jspi.2015.08.005zbMath1383.62154OpenAlexW1216217498MaRDI QIDQ899351

Chaitra H. Nagaraja, Tucker S. McElroy

Publication date: 28 December 2015

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2015.08.005

zbMATH Keywords

heavy tailsstable distributionsextreme value theory

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Statistics of extreme values; tail inference (62G32)

Related Items

Weighted least squares estimators for the Parzen tail index ⋮ On the measurement and treatment of extremes in time series

Cites Work

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• Tail index estimation in the presence of long-memory dynamics
• The tail empirical process for long memory stochastic volatility sequences
• On the favorable estimation for fitting heavy tailed data
• Using the bootstrap to estimate mean squared error and select smoothing parameter in nonparametric problems
• A moment estimator for the index of an extreme-value distribution
• Tail exponent estimation via broadband log density-quantile regression
• Statistical inference using extreme order statistics
• A simple general approach to inference about the tail of a distribution
• Optimal choice of sample fraction in extreme-value estimation
• Asymptotic normality of least-squares estimators of tail indices
• Generalized least-squares estimators for the thickness of heavy tails
• Approximation by penultimate extreme value distributions
• Penultimate approximations in statistics of extremes and reliability of large coherent systems
• The harmonic moment tail index estimator: asymptotic distribution and robustness
• On Choosing an Estimate of the Spectral Density Function of a Stationary Time Series
• Comparison of tail index estimators
• Simple Robust Testing of Regression Hypotheses
• Asymptotic Behavior of Central Order Statistics under Monotone Normalization
• Optimal Bandwidth Selection in Heteroskedasticity–Autocorrelation Robust Testing