Numerical Estimation for the Three-Parameter Burr-XII Model Based on Picard’s Method

Abstract

Abstract In parameter estimation techniques, there are several methods for estimating the distribution parameters in life data analysis. However, most of them are less efficient than Bayes’ method based on the informative prior. Thus, the main objective of this study is to present an optimal numerical technique, Picard’s method, for estimating Burr type-XII distribution parameters and compare them with Bayes’ method based on the informative gamma and kernel priors. A comparison between these estimators is provided using an extensive Monte Carlo simulation. The simulation results indicated that Picard’s method is highly favorable, which provides better estimates and outperforms Bayes’ method based on the generalized progressive hybrid censoring scheme. Finally, two real dataset analyses are presented to illustrate the efficiency of the proposed methods.