Numerical Inference on Generalized Gamma Distribution Parameters Using Runge-Kutta Method

Abstract

AbstractIn parameter estimation techniques, there are many 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 estimation method using a numerical method such as the Runge-Kutta technique for estimating the Generalized Life Model parameters and comparing them with the Bayesian estimates based on the informative gamma and kernel priors. An extensive Monte Carlo simulation has been carried based on the generalized progressive hybrid censoring scheme in terms of two criteria, namely, absolute bias and mean squared error. 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 dataset analyses are presented to illustrate the efficiency of the proposed methods.