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  • 标题:Functional Kernel Estimation of the Conditional Extreme Quantile under Random Right Censoring
  • 本地全文:下载
  • 作者:Justin Ushize Rutikanga ; Aliou Diop
  • 期刊名称:Open Journal of Statistics
  • 印刷版ISSN:2161-718X
  • 电子版ISSN:2161-7198
  • 出版年度:2021
  • 卷号:11
  • 期号:1
  • 页码:162-177
  • DOI:10.4236/ojs.2021.111009
  • 语种:English
  • 出版社:Scientific Research Publishing
  • 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.
  • 关键词:Kernel Estimator;Functional Data;Censored Data;Conditional Extreme Quantile;Heavy-Tailed Distributions
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