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Bayesian Prediction Based on Type-I Hybrid Censored Data from a General Class of Distributions

Received: 4 May 2016     Accepted: 12 May 2016     Published: 14 June 2016
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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.

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

Keywords

Bayesian Prediction, Type-I Hybrid Censored, General Class, Markov Chain Monte Carlo, Importance Sampling Technique

References
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[15] Guure, C. B., Ibrahim, N. A. and Al Omari, A. M., 2012. Bayesian estimation of two-parameter Weibull distribution using extension of Jeffreys’ prior information with three loss functions, Math Probl Eng. Article ID 589640, 13 pages.
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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

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    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

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    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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  • @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}
    }
    

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    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
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    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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Author Information
  • Department of Mathematics, Faculty of Science, Al-Azhar University, Cairo, Egypt

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