Bayesian and non-Bayesian inference for inverse Weibull model based on jointly type-II hybrid censoring samples with modeling to physics data

Abstract

In recent years, there has been a lot of interest in the research of cooperative censoring schemes. In this work, we compared the relative benefits of two competing length-of-life products using inverse Weibull lifetime products with a joint type-II hybrid censoring scheme (JHC-Type II). We initially examined the maximum likelihood estimators and their confidence intervals (CIs) for the unknown parameters based on JHC-Type II. Then, under the premise of independent gamma priors, we offer Bayes estimates of the parameters using squared error loss and LINEX loss functions. We used the Markov chain Monte Carlo method to create credible intervals and Bayesian estimates. Based on the parametric bootstrapping techniques known as Boot-p and Boot-t, we create two bootstrapping CIs. In addition, we do a Monte Carlo simulation experiment to track how well the aforementioned approaches work and to determine the corresponding confidence and credible intervals. Finally, to show how the approaches covered in this paper might be used, we consider a real physical dataset.