Confidence regions for high quantiles of a heavy tailed distribution

DOI10.1214/009053606000000416zbMath1246.62125arXivmath/0611278OpenAlexW3099948353MaRDI QIDQ449958

Liang Peng, Yongcheng Qi

Publication date: 3 September 2012

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/math/0611278

zbMATH Keywords

empirical likelihood methoddata tilting

Mathematics Subject Classification ID

Related Items (17)

Bayesian inference for extreme quantiles of heavy tailed distributions ⋮ Interval estimation of value-at-risk based on GARCH models with heavy-tailed innovations ⋮ Tilting Methods for Assessing the Influence of Components in a Classifier ⋮ Nonparametric estimation of operational value-at-risk (OpVaR) ⋮ Confidence intervals for ARMA-GARCH value-at-risk: the case of heavy tails and skewness ⋮ Statistical inference for conditional quantiles in nonlinear time series models ⋮ Smoothed jackknife empirical likelihood method for tail copulas ⋮ Inference of high quantiles of a heavy-tailed distribution from block data ⋮ Empirical likelihood inference for Haezendonck-Goovaerts risk measure ⋮ On the tail index of a heavy tailed distribution ⋮ Location invariant Weiss-Hill estimator ⋮ A class of unbiased location invariant Hill-type estimators for heavy tailed distributions ⋮ Asymptotic normality of location invariant heavy tail index estimator ⋮ Simultaneous confidence bands for time-series prediction function ⋮ Bootstrap and empirical likelihood methods in extremes ⋮ Reduce computation in profile empirical likelihood method ⋮ Assessing the performance of confidence intervals for high quantiles of Burr XII and Inverse Burr mixtures

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Empirical likelihood ratio confidence regions
• A simple general approach to inference about the tail of a distribution
• Likelihood based confidence intervals for the tail index
• Empirical-likelihood-based confidence interval for the mean with a heavy-tailed distribution.
• Empirical likelihood is Bartlett-correctable
• Methodology and Algorithms of Empirical Likelihood
• Estimation of quantiles of the maximum of N observations
• Empirical likelihood ratio confidence intervals for a single functional
• Estimation of Parameters and Larger Quantiles Based on the k Largest Observations
• Comparison of tail index estimators
• On optimising the estimation of high quantiles of a probability distribution
• Data Tilting for Time Series
• Estimating the First- and Second-Order Parameters of a Heavy-Tailed Distribution
• Generalizations of the Hill estimator -- asymptotic versus finite sample behaviour

This page was built for publication: Confidence regions for high quantiles of a heavy tailed distribution