Parameter Estimation of Gompertz Distribution Under Constant Stress Accelerated Life Testing Using Progressive Type-II Censoring

Abstract

In this paper, the problem of estimation of parameters of Gompertz distribution under constant stress accelerated life testing model using a progressively type II censored sample is considered. The maximum likelihood and Bayesian methods are used for estimating the unknown parameters. Markov chain Monte Carlo techniques are employed to carry out Bayesian estimation. A simulation study is carried out to assess and compare the different estimators discussed in this paper. Finally, real data are analyzed to illustrate the results.