Bayesian inference based on a jointly type-II censored sample from two exponential populations

Bayesian and Non–Bayesian Estimation for Two Generalized Exponential Populations Under Joint Type II Censored Scheme

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AbstractIn this paper, Bayesian and non-Bayesian estimators have been obtained for two generalized exponential populations under joint type II censored scheme, which generalize results of Balakrishnan and Rasouli (2008) and Shafay et al (2013). The maximum likelihood estimators (MLEs) of the parameters and Bayes estimators have been developed under squared error loss function as well as under LINEX loss function.

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“…They developed inferential methods based on maximum likelihood estimates (MLE) and compared their performance with those based on some other approaches such as Bootstrap. Shafay et al (2013) derived the Bayesian inference for the unknown parameters of two exponential populations under joint type II censoring they developed with the use of squared-error, linear-exponential and general entropy loss functions. The problem of predicting the future failure times, both point and interval prediction, based on the observed joint type-II censored data is obtained; see also Rasouli and Balakrishnan (2010) …”

Statistical inferences with jointly type-II censored samples from two Pareto distributions

“…Box 32691, Jeddah 21438, Saudi Arabia, E-mail: habuzinadah@kau.edu.sa tra [5], Mehrotra and Bhattacharyya [6]. Balakrishnan and Rasouli [7], Rasouli and Balakrishnan [8], and recently Shafay et al [9]. Recently, Kundu [10] prsented new type-II progressive censoring scheme for two samples.…”

Parameter estimation for multiple weibull populations under joint type-II censoring

“…The problem of predicting the future failure times, both point and interval prediction, based on the observed joint type-II censored data is obtained; see also Rasouli and Balakrishnan [8] for a generalization of their results to progressive type-II censoring for the parameters of two exponential populations. Balakrishnan and Feng [4] generalized Balakrishnan and Rasouli [3], Rasouli and Balakrishnan [8] and Shafay et al [10] works by considered a jointly type II censored sample arising from h independent exponential populations. Ashour and Abo-Kasem [1] derived Bayesian and nonBayesian estimators for two generalized exponential populations under joint type II censored scheme.…”

“…They developed inferential methods based on maximum likelihood estimates (MLE) and compared their performance with those based on some other approaches such as Bootstrap. Shafay et al [10] derived the Bayesian inference for the unknown parameters of two exponential populations under joint type II censoring they developed with the use of squared-error, linear-exponential and general entropy loss functions. The problem of predicting the future failure times, both point and interval prediction, based on the observed joint type-II censored data is obtained; see also Rasouli and Balakrishnan [8] for a generalization of their results to progressive type-II censoring for the parameters of two exponential populations.…”