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.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 5, Issue 4) |
| DOI | 10.11648/j.ajtas.20160504.15 |
| Page(s) | 192-201 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2016. Published by Science Publishing Group |
Bayesian Prediction, Type-I Hybrid Censored, General Class, Markov Chain Monte Carlo, Importance Sampling Technique
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APA Style
Amr Sadek. (2016). Bayesian Prediction Based on Type-I Hybrid Censored Data from a General Class of Distributions. American Journal of Theoretical and Applied Statistics, 5(4), 192-201. https://doi.org/10.11648/j.ajtas.20160504.15
ACS Style
Amr Sadek. Bayesian Prediction Based on Type-I Hybrid Censored Data from a General Class of Distributions. Am. J. Theor. Appl. Stat. 2016, 5(4), 192-201. doi: 10.11648/j.ajtas.20160504.15
AMA Style
Amr Sadek. Bayesian Prediction Based on Type-I Hybrid Censored Data from a General Class of Distributions. Am J Theor Appl Stat. 2016;5(4):192-201. doi: 10.11648/j.ajtas.20160504.15
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Download@article{10.11648/j.ajtas.20160504.15,
author = {Amr Sadek},
title = {Bayesian Prediction Based on Type-I Hybrid Censored Data from a General Class of Distributions},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {5},
number = {4},
pages = {192-201},
doi = {10.11648/j.ajtas.20160504.15},
url = {https://doi.org/10.11648/j.ajtas.20160504.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20160504.15},
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.},
year = {2016}
}
TY - JOUR T1 - Bayesian Prediction Based on Type-I Hybrid Censored Data from a General Class of Distributions AU - Amr Sadek Y1 - 2016/06/14 PY - 2016 N1 - https://doi.org/10.11648/j.ajtas.20160504.15 DO - 10.11648/j.ajtas.20160504.15 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 192 EP - 201 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20160504.15 AB - 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. VL - 5 IS - 4 ER -
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