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

Abstract It is widely known that structural probability intervals are usually just as effective as Bayesian probability intervals. However, the literature didn’t contain point estimation for the distribution parameters. Therefore, the main objective of this work is to find the structural point estimate for the Weibull distribution parameters based on the generalized progressive hybrid-censoring scheme. These estimates are compared to the Bayesian estimates via Monte Carlo simulation. The simulation results indicated that structural inference is highly efficient and provides better estimates than Bayesian inference based on different priors. Finally, two real datasets are analysed to demonstrate the efficiency of the proposed methods.