Functional Kernel Estimation of the Conditional Extreme Quantile under Random Right Censoring

• 作者: Justin Ushize Rutikanga Aliou Diop
• 来源期刊: 统计学期刊(英文)
• 学科: 数学
• 关键词: Kernel Estimator Functional Data Censored Data Conditional Extreme Quantile Heavy-Tailed Distributions
• 年，卷（期）: 2021,（1）
• 页码范围: 162-177
• 页数: 16页
• 分类号: O17

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 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>