Estimation of extreme quantiles from heavy-tailed distributions in a location-dispersion regression model

DOI10.1214/20-EJS1779zbMath1460.62065MaRDI QIDQ2219217

Stéphane Girard, El Hadji Dème, Aboubacrène Ag Ahmad, Aliou Diop, Antoine Usseglio-Carleve

Publication date: 19 January 2021

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1609384077

zbMATH Keywords

semiparametric estimationextreme conditional quantiletail-indexregression and dispersion functions

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic distribution theory in statistics (62E20) Applications of statistics to environmental and related topics (62P12) Order statistics; empirical distribution functions (62G30) Statistics of extreme values; tail inference (62G32) Seismology (including tsunami modeling), earthquakes (86A15) Large deviations (60F10)

Related Items (5)

Estimation of extreme quantiles from heavy-tailed distributions with neural networks ⋮ Statistical inference for extreme extremile in heavy-tailed heteroscedastic regression model ⋮ Statistical inference on a changing extreme value dependence structure ⋮ Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models ⋮ Extreme partial least-squares

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