Extremes

, Volume 21, Issue 1, pp 57–95 | Cite as

Tail dimension reduction for extreme quantile estimation

Article

Abstract

In a regression context where a response variable Y is recorded with a covariate X p , two situations can occur simultaneously: (a) we are interested in the tail of the conditional distribution and not on the central part of the distribution and (b) the number p of regressors is large. To our knowledge, these two situations have only been considered separately in the literature. The aim of this paper is to propose a new dimension reduction approach adapted to the tail of the distribution in order to propose an efficient conditional extreme quantile estimator when the dimension p is large. The results are illustrated on simulated data and on a real dataset.

Keywords

Regression Extreme quantile Dimension reduction Kernel smoothing 

AMS 2000 Subject Classifications

62G32 62G08 62G05 62G20 

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Université de Strasbourg, CNRS, IRMA UMR 7501StrasbourgFrance

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