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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Author
Amr Sadek, Department of Mathematics, Faculty of Science, Al-Azhar University, Cairo, Egypt
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Abstract
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.
Keywords
Bayesian Prediction, Type-I Hybrid Censored, General Class, Markov Chain Monte Carlo, Importance Sampling Technique
To cite this article
Amr Sadek, 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. doi: 10.11648/j.ajtas.20160504.15
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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