Estimation of the Weibull Extension Model Parameters Based on Adams’s Method

Abstract In parameter estimation techniques, the Bayes method is the most commonly used technique in social sciences and psychology, despite its subjectivity to prior information other than data. Thus, the main objective of this paper is to introduce a numerical estimation method, which is the Adams’ method for deriving the estimators for the Weibull extension model parameters and compared to the Bayes method based on different priors via Monte Carlo simulations. The simulation results are strongly favorable to Adams’ method, which provides better estimates and outperforms Bayes’ method. Finally, numerical examples are given to demonstrate the efficiencies of the proposed methods based on the generalized progressive hybrid censoring data.