Reliability estimation for inverse Pareto lifetime model based on unified hybrid censored data

Censoring plays an important role in the reliability and life testing trials due to its cost optimality and time reduction properties. The unified hybrid censoring scheme is the combination of the generalized type-I and type-II hybrid censoring schemes. In this paper, our objective is to study the classical and Bayesian estimation methods of the parameter and reliability characteristics from the inverse Pareto lifetime model under the unified hybrid censoring scheme. In the classical estimation methods, the maximum likelihood and associated asymptotic confidence interval estimators are derived. In Bayesian estimation, the Bayes estimators under squared error loss function and the highest posterior density (HPD) credible intervals based on the informative and non-informative priors are developed. For the Bayesian computations, the Markov chain Monte Carlo techniques are used to compute Bayes and HPD credible interval estimates. A quantitative outcome of the objectives has been shown by a Monte Carlo simulation and with the help of a real-life application.