Estimating the conditional tail index by integrating a kernel conditional quantile estimator

DOI10.1016/j.jspi.2012.01.011zbMath1242.62039OpenAlexW1995878269MaRDI QIDQ434577

A. Schorgen, Armelle Guillou, Laurent Gardes

Publication date: 16 July 2012

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

Full work available at URL: https://hal.inria.fr/inria-00578479/file/kernelgam.pdf

zbMATH Keywords

asymptotic normalityheavy-tailed distributioncovariateskernel estimator

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Statistics of extreme values; tail inference (62G32)

Related Items

Estimation for Extreme Conditional Quantiles of Functional Quantile Regression ⋮ Extreme Quantile Estimation Based on the Tail Single-index Model ⋮ Non-parametric Estimation of Extreme Risk Measures from Conditional Heavy-tailed Distributions ⋮ Estimation of the tail-index in a conditional location-scale family of heavy-tailed distributions ⋮ A note on tail dependence regression ⋮ Estimation of High Conditional Quantiles for Heavy-Tailed Distributions ⋮ Estimation of Extreme Conditional Quantiles Through Power Transformation

Cites Work

• Unnamed Item
• Unnamed Item
• Conditional extremes from heavy-tailed distributions: an application to the estimation of extreme rainfall return levels
• Semi-parametric estimation for heavy tailed distributions
• A moment estimator for the index of an extreme-value distribution
• Regression with response distributions of Pareto-type
• A moving window approach for nonparametric estimation of the conditional tail index
• Kernel estimators for the second order parameter in extreme value statistics
• Bias-reduced estimators of the Weibull tail-coefficient
• Functional nonparametric estimation of conditional extreme quantiles
• A simple general approach to inference about the tail of a distribution
• On exponential representations of log-spacings of extreme order statistics
• Tail index estimation and an exponential regression model
• Extreme value analysis of environmental time series: an application to trend detection in ground-level ozone. With comments and a rejoinder by the author
• Kernel estimators of extreme level curves
• A class of asymptotically unbiased semi-parametric estimators of the tail index.
• Estimating a tail exponent by modelling departure from a Pareto distribution
• Nonparametric functional data analysis. Theory and practice.
• The qq-estimator and heavy tails
• Local Likelihood Smoothing of Sample Extremes
• Generalized Additive Modelling of Sample Extremes