Worms, J. and Worms, R. (2014) New Estimators of the Extreme Value Index under Random Right Censoring, for Heavy-Tailed Distributions. Extremes, 17, 337-358.
https://doi.org/10.1007/s10687-014-0189-6

has been cited by the following article:

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

  AUTHORS: Justin Ushize Rutikanga, Aliou Diop

  KEYWORDS: Kernel Estimator, Functional Data, Censored Data, Conditional Extreme Quantile, Heavy-Tailed Distributions

  JOURNAL NAME: Open Journal of Statistics, Vol.11 No.1, February 7, 2021

  ABSTRACT: 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.