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Predictive Inference from the Exponentiated Weibull Model Given Adaptive Progressive Censored Data |
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PP: 1177-1184 |
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doi:10.18576/amis/100336
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Author(s) |
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Abdullah Y. Al-Hossain,
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Abstract |
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| Adaptive progressive censoring schemes have been shown to be useful in striking a balance between statistical estimation
efficiency and the time spent on a life-testing experiment. In this paper, the problem of predicting the future order statistics and future
upper record values based on observed adaptive progressive Type-II censored samples from exponentiated Weibull (EW) distribution
is addressed. Using the Bayesian approach and the two-sample scheme, the predictive and survival functions are derived and then the
interval predictions of the future samples are obtained. Two-sample Bayesian predictive survival function can not be obtained in closedform
and so Gibbs sampling procedure is used to draw Markov Chain Monte Carlo (MCMC) samples, which are then used to compute
the approximate predictive survival function. The paper also includes an illustration of our method in examples about breaking stress
of carbon fibres. |
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