Abstract
In this paper, generalized progressive hybrid censoring is discussed, while a scheme is designed to provide a flexible and symmetrical scenario to collect failure information in the whole life cycle of units. When the lifetime of units follows Kumaraswamy distribution, inference is investigated under classical and Bayesian approaches. The maximum likelihood estimates and associated existence and uniqueness properties are established and the confidence intervals for unknown parameters are provided by using a large sample size based on asymptotic theory. Moreover, the Bayes estimates along with highest probability density credible intervals are also developed through the Monte-Carlo Markov Chain sampling technique to approximate the associated posteriors. Simulation studies and a real-life example are presented for illustration purposes.
Highlights
Nowadays, sample size heavily affects the accuracy of the estimation and complete testing is impossible to conduct in practical experiments due to the progress of manufacturing design and technology, which yields high reliability and a long span of modern products
It is proper to use bounded models to fit real-life data which may assign more weight to failure data and provide a better inferential accuracy. Motivated by reasons such as those mentioned above, this paper is devoted to discussing the statistical inferential problem for a bounded lifetime distribution with range (0, 1), and classical and Bayesian approaches are used for parameter estimation under generalized progressive hybrid censoring
It can be seen from the simulation results that the performance of maximum likelihood estimators (MLEs) and Bayes estimates are satisfactory; whereas the Bayes estimates are obtained under almost non-informative priors, they are slightly superior to MLEs in terms of Average bias (AB) and MSEs
Summary
Sample size heavily affects the accuracy of the estimation and complete testing is impossible to conduct in practical experiments due to the progress of manufacturing design and technology, which yields high reliability and a long span of modern products In this situation, a censoring scheme (CS) has been introduced due to many reasons such as time constraint and cost reduction. It is proper to use bounded models to fit real-life data which may assign more weight to failure data and provide a better inferential accuracy Motivated by reasons such as those mentioned above, this paper is devoted to discussing the statistical inferential problem for a bounded lifetime distribution with range (0, 1), and classical and Bayesian approaches are used for parameter estimation under generalized progressive hybrid censoring.
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