Tail dimension reduction for extreme quantile estimation

DOI10.1007/s10687-017-0300-xzbMath1396.62097OpenAlexW2409075483MaRDI QIDQ1744176

Laurent Gardes

Publication date: 16 April 2018

Published in: Extremes (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10687-017-0300-x

zbMATH Keywords

kernel smoothingdimension reductionregressionextreme quantile

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Statistics of extreme values; tail inference (62G32)

Related Items (11)

Conditional marginal expected shortfall ⋮ Estimation for Extreme Conditional Quantiles of Functional Quantile Regression ⋮ Extreme Quantile Estimation Based on the Tail Single-index Model ⋮ Efficient estimation of partially linear tail index models using B‐splines ⋮ Tail inverse regression: dimension reduction for prediction of extremes ⋮ Gradient boosting for extreme quantile regression ⋮ Nonparametric confidence intervals for conditional quantiles with large-dimensional covariates ⋮ Principal component analysis for multivariate extremes ⋮ A nonparametric estimator for the conditional tail index of Pareto-type distributions ⋮ Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models ⋮ Extreme partial least-squares

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• Comment

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