Abstract
Using progressive Type-II censoring data, this study deals with the estimation of param-eters of the Unit-Weibull distribution using two classical methods and the Bayesian method. In the classical methods, maximum likelihood and the maximum product of spacing (MPS) methods are used to obtain the parameters of the model by utilizing the Newton-Raphson method. On the basis of observed Fisher information matrix, approximate confidence intervals for the unknown param-eters are obtained. In addition, two bootstrap methods are used to obtain confidence intervals for the unknown parameters of the model. In the Bayesian estimation, we have considered both like-lihood function as well as product of spacing function to estimate the model parameters. Bayes esti-mators are obtained under squared error loss function using independent gamma density priors for the unknown model parameters. Since closed-form of the Bayes estimators is not available, the Metropolis-Hastings algorithm is proposed to approximate the Bayes estimates. In addition, high-est posterior density credible intervals are obtained. Further, using different optimally criteria, an optimal scheme has been proposed. A simulation study is conducted to assess the statistical perfor-mance of all the estimators. To demonstrate the proposed methodology a real data analysis is pro-vided to illustrate all the statistical inferential procedures developed in the paper.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/ 4.0/).