Conditional Inference on the Generalized Shape-Scale Family

Abstract

In parameter estimation techniques, there are many methods for estimating the distribution parameters in life data analyses. However, most of them are less efficient than the Bayesian method, despite its subjectivity. Thus, the main objective of this study is to present the conditional inference method as an alternative and efficient method for estimating the generalized shape-scale family parameters and comparing them with the Bayesian estimates. A comparison between these estimators is provided by using an extensive Monte Carlo simulation study based on two criteria, namely, the absolute average bias and mean squared error based on the generalized progressive hybrid censoring scheme. The simulation results indicated that the conditional inference is highly efficient, which provides better estimates and outperforms the Bayesian inference. Finally, two real dataset analyses are presented to illustrate the efficiency of the proposed methods.