This paper deals with the estimation procedure for inverse Weibull distribution under progressive type-II censored samples when removals follow Beta-binomial probability law. To estimate the unknown parameters, the maximum likelihood and Bayes estimators are obtained under progressive censoring scheme mentioned above. Bayes estimates are obtained using Markov chain Monte Carlo (MCMC) technique considering square error loss function and compared with the corresponding MLE's. Further, the expected total time on test is obtained under considered censoring scheme. Finally, a real data set has been analysed to check the validity of the study.
Shannon’s entropy plays important role in the information theory. However, it can’t be applied to systems which have survived for some time. Therefore, the concept of residual entropy was developed. In this paper, the estimation of the entropy of a two-parameter inverse Weibull distribution based on the generalized type-II hybrid censored sample is considered. The Bayes estimator for the residual entropy of the Inverse Weibull distribution under the generalized type-II hybrid censored sample is given. Simulation experiments are conducted to see the effectiveness of the different estimators.
The combination of generalization Type-I hybrid censoring and generalization Type-II hybrid censoring schemes, scheme creates a new censoring called a Unified hybrid censoring scheme. Therefore, in this study, the E-Bayesian estimation of parameters of the inverse Weibull (IW) distribution is obtained under the unified hybrid censoring scheme, and the efficiency of the proposed method was compared with the Bayesian estimator using Monte Carlo simulation and a real data set.
Bayesian Estimation for Inverse Weibull Distribution Under Progressive Type-II Censored Data With Beta-Binomial Removals