Bayesian Prediction Based on Type-I Hybrid Censored Data from a General Class of Distributions
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 4, July 2016, Pages: 192-201
Received: May 4, 2016;
Accepted: May 12, 2016;
Published: Jun. 14, 2016
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Amr Sadek, Department of Mathematics, Faculty of Science, Al-Azhar University, Cairo, Egypt
One and two-sample Bayesian prediction intervals based on Type-I hybrid censored for a general class of distribution 1-F(x)=[ah (x)+b]c are obtained. For the illustration of the developed results, the inverse Weibull distribution with two unknown parameters and the inverted exponential distribution are used as examples. Using the importance sampling technique and Markov Chain Monte Carlo (MCMC) to compute the approximation predictive survival functions. Finally, a real life data set and a generated data set are used to illustrate the results derived here.
Bayesian Prediction Based on Type-I Hybrid Censored Data from a General Class of Distributions, American Journal of Theoretical and Applied Statistics.
Vol. 5, No. 4,
2016, pp. 192-201.
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