Classical and Bayesian Inference of the Inverse Nakagami Distribution Based on Progressive Type-II Censored Samples

This paper explores statistical inferences when the lifetime of product follows inverse Nakagami distribution using progressive Type-II censored data. Likelihood-based and maximum spacing (MPS)-based methods are considered for estimating parameters model. In addition, approximate confidence intervals constructed via asymptotic theory both likelihood functions. Based on traditional functions, Bayesian estimates also under a squared error loss function non-informative priors, Gibbs sampling based MCMC algorithm is proposed to Bayes estimates, where highest posterior density credible obtained. Numerical studies presented compare estimators Monte Carlo simulations. To demonstrate methodology in real-life scenario, well-known data set agricultural machine elevators with high defect rates analyzed illustration.