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

Abstract

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.