%0 Journal Article
%T Functional Kernel Estimation of the Conditional Extreme Quantile under Random Right Censoring
%A Justin Ushize Rutikanga
%A Aliou Diop
%J Open Journal of Statistics
%P 162-177
%@ 2161-7198
%D 2021
%I Scientific Research Publishing
%R 10.4236/ojs.2021.111009
%X The study of estimation of conditional extreme
quantile in incomplete data frameworks is of growing interest. Specially, the
estimation of the extreme value index in a censorship framework has been the
purpose of many inves<span style=\"font-family:Verdana;\">tigations when finite
dimension covariate information has been considered. In this paper, the
estimation of the conditional extreme quantile of a </span><span style=\"font-family:Verdana;\">heavy-tailed
distribution is discussed when some functional random covariate (</span><i><span style=\"font-family:Verdana;\">i.e.</span></i><span style=\"font-family:Verdana;\"> valued in some infinite-dimensional
space) information is available and the scalar response variable is
right-censored. A Weissman-type estimator of conditional extreme quantiles is
proposed and its asymptotic normality is established under mild assumptions. A
simulation study is conducted to assess the finite-sample behavior of the
proposed estimator and a comparison with two simple estimations strategies is
provided.</span>
%K Kernel Estimator
%K Functional Data
%K Censored Data
%K Conditional Extreme Quantile
%K Heavy-Tailed Distributions
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=107073