AIP Advances (Nov 2023)

Classical and Bayesian estimation for Gompertz distribution under the unified hybrid censored sampling with application

  • O. E. Abo-Kasem,
  • A. Abdelgaffar,
  • Aned Al Mutairi,
  • Rana H. Khashab,
  • Wael S. Abu El Azm

DOI
https://doi.org/10.1063/5.0174543
Journal volume & issue
Vol. 13, no. 11
pp. 115206 – 115206-15

Abstract

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This study discusses the Gompertz distribution’s statistical inference using unified hybrid censored data. Under various loss functions, the maximum likelihood and Bayesian approaches are explored for estimating the parameters for the Gompertz distribution. To compare the suggested methodologies, the Monte Carlo simulation is used, which also introduces the simulation research. By examining a genuine dataset, the applicability of the presented inference in practice is finally demonstrated.