Conditional Inference on the Inverse Weibull Distribution Parameters

Abstract

AbstractIt is widely known that confidence intervals based on conditional inference are usually just as effective as Bayesian confidence intervals based on the non-informative prior. However, they are less efficient than those based on the Bayesian inference using the informative prior distribution. Therefore, this paper's main objective is to find the conditional point estimates using the pivotal functions for the inverse Weibull distribution parameters, based on the generalized progressive hybrid-censoring scheme, and compare them with the Bayesian estimates, via Monte Carlo simulation. The simulation results showed that the conditional inference is highly efficient and provides better estimates than the Bayesian estimates based on different loss functions. Finally, the proposed model could be necessary for analysing real data to demonstrate the efficiencies of the proposed methods.