Functional Kernel Estimation of the Conditional Extreme Quantile under Random Right Censoring
Functional Kernel Estimation of the Conditional Extreme Quantile under Random Right Censoring作者机构：Institut de Mathématiques et de Sciences Physiques (IMSP-UAC) Porto-Novo Bénin LERSTAD Université Gaston Berger Saint Louis Sénégal
出 版 物：《Open Journal of Statistics》 (统计学期刊（英文）)
年 卷 期：2021年第11卷第1期
页 面：162-177页
学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学]
主 题：Kernel Estimator Functional Data Censored Data Conditional Extreme Quantile Heavy-Tailed Distributions
摘 要：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 investigations when finite dimension covariate information has been considered. In this paper, the estimation of the conditional extreme quantile of a heavy-tailed distribution is discussed when some functional random covariate (i.e. 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.
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