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B0281
Title: Tail dimension reduction for extreme quantile estimation Authors:  Laurent Gardes - University of Strasbourg (France) [presenting]
Abstract: In a regression context where a real response variable $Y$ is recorded with a $p$-dimensional covariate $X$, 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. Up to our knowledge, these two situations have only been considered separately in the literature. The aim 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.