Bayesian and Non-Bayesian Inference for Weibull Inverted Exponential Model under Progressive First-Failure Censoring Data

In this article, the estimation of parameters and reliability hazard functions for Weibull inverted exponential (WIE) distribution is considered based on progressive first-failure censoring (PFFC) data. For non-Bayesian inference, maximum likelihood (ML) estimators are acquired; meanwhile, their existence verified. Via asymptotic normality ML delta method, corresponding confidence intervals (CIs) constructed. Bayesian Lindley’s approximation Markov chain Monte Carlo (MCMC) techniques proposed to obain Bayes credible (CRIs). To end, both symmetric asymmetric loss used. A large number simulations implemented evaluate efficiency developed methods. Eventually, a numerical example analyzed illustrative purposes.