Statistical Inference on the Three Parameters Burr-XII Distribution Using the Runge-Kutta Method

Abstract In parameter estimation techniques, there are many methods for estimating the distribution parameters in life data analysis. However, most of them are less efficient than the Bayes method based on the informative prior. Thus, the main objective of this study is to present an optimal estimation method using the Runge-Kutta technique for estimating the three parameters of the Burr type-XII distribution. The Runge-Kutta estimates are compared with the Bayesian estimates based on the informative gamma and kernel priors via an extensive Monte Carlo simulation. The simulation results indicated that the Runge-Kutta method is highly favorable, which provides better estimates and outperforms the Bayes method based on the generalized progressive hybrid censoring scheme. Finally, two real datasets are presented to illustrate the efficiency of the proposed methods.