Conditional Inference on the Weibull Distribution Parameters Based on the Generalized Progressive Hybrid Censoring Scheme

Abstract

Abstract It is widely known that conditional inference is usually just as effective as Bayesian inference based on a non-informative prior. However, it is less efficient than Bayesian inference based on the informative prior distribution. Therefore, the main objective is to find the conditional point estimates using pivotal functions for the Weibull distribution parameters, based on the generalized progressive hybrid-censoring scheme, and compare it with the Bayesian estimates, via Monte Carlo simulation. The simulation results showed that conditional inference is highly efficient and provides better estimates than Bayesian estimates based on different loss functions. Finally, the proposed model could be important for analysing real data to demonstrate the efficiencies of the proposed methods.