Abstract
Abstract
In parameter estimation techniques, the Bayesian estimation method is the most common technique that has been used in social science and psychology, despite the fact that it is subjective to prior information other than data, which worries some statisticians. Thus, the main objective of this paper is to introduce a numerical iteration technique, which is Picard’s method for estimating the distribution parameters. This method has been applied for deriving the estimators for the Weibull extension model parameters and compared with the Bayes method via Monte Carlo simulations. The simulation results indicate that Picard’s method is strongly favorable because it provides better estimates and outperforms the Bayes method. Finally, numerical examples are given to demonstrate the efficiency of the proposed methods.
Publisher
Research Square Platform LLC
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