Statistical Inference and Optimum Life Testing Plans Under Type-II Hybrid Censoring Scheme
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This article considers estimation of unknown parameters and prediction of future observations of a generalized exponential distribution based on Type-II hybrid censored data. Bayes point and HPD interval estimates of the unknown parameters are obtained under the assumption of independent gamma priors. Different classical and Bayesian point predictors and prediction intervals are obtained in two-sample situation against squared error loss function. The optimum censoring schemes are computed under various optimality criteria. Monte Carlo simulations are performed to compare different methods and two data sets are analyzed for illustrative purposes.
KeywordsBayes estimates EM algorithm Generalized exponential distribution MH algorithm Prediction Optimal censoring
The authors thank one anonymous reviewer and an associate editor for their critical comments and helpful suggestions which have resulted in an improvement over the earlier version of this article. The research of the corresponding author is partially supported by Consejo Nacional de Ciencia y Tecnología (CONACYT) Grant Number CB 2016-2018 No. 252996.
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