Bhattacharya, S., Kallitsis, M. and Stoev, S. (2019) Data-Adaptive Trimming of the Hill Estimator and Detection of Outliers in the Extremes of Heavy-Tailed Data. Electronic Journal of Statistics, 13, 1872-1925.
https://doi.org/10.1214/19-EJS1561

has been cited by the following article:

• TITLE: Distributed Trimmed Hill Estimator

  AUTHORS: Tao Guo

  KEYWORDS: Extreme Value Index, Distributed Trimmed Hill Estimator

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.11 No.12, December 27, 2023

  ABSTRACT: Proceeded from trimmed Hill estimators and distributed inference, a new distributed version of trimmed Hill estimator for heavy tail index is proposed. Considering the case where the number of observations involved in each machine can be either the same or different and either fixed or varying to the total sample size, its consistency and asymptotic normality are discussed. Simulation studies are particularized to show the new estimator performs almost in line with the trimmed Hill estimator.