Analysis of Progressively Censored Repair Time of Airborne Communication Transceiver with Burr-Hatke Exponential Model

In this chapter, the parameter estimation of a Burr-Hatke exponential model based on the progressive type-II censored sample is investigated. Various methods of estimation for complete data are generalized to the case under progressive censored samples. These approaches comprise maximum likelihood, least squares, maximum product spacings, and Bayesian estimation. Interval estimate and coverage probability for the parameter are derived by the use of maximum likelihood and Bayesian estimation techniques. Markov chain Monte Carlo algorithm has been employed to obtain the Bayes estimator of the parameter with gamma prior under squared error loss function. A vast comparative analysis of the four methods is made using a Monte Carlo empirical study. The empirical findings are used in the formulation of certain suggestions, and a real-world data example is shown to illustrate how the developed theory may be applied in practice.