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Expected Bayesian estimation for exponential model based on simple step stress with Type-I hybrid censored data


  • Received: 24 May 2022 Revised: 26 June 2022 Accepted: 29 June 2022 Published: 08 July 2022
  • The procedure of selecting the values of hyper-parameters for prior distributions in Bayesian estimate has produced many problems and has drawn the attention of many authors, therefore the expected Bayesian (E-Bayesian) estimation method to overcome these problems. These approaches are used based on the step-stress acceleration model under the Exponential Type-I hybrid censored data in this study. The values of the distribution parameters are derived. To compare the E-Bayesian estimates to the other estimates, a comparative study was conducted using the simulation research. Four different loss functions are used to generate the Bayesian and E-Bayesian estimators. In addition, three alternative hyper-parameter distributions were used in E-Bayesian estimation. Finally, a real-world data example is examined for demonstration and comparative purposes.

    Citation: M. Nagy, M. H. Abu-Moussa, Adel Fahad Alrasheedi, A. Rabie. Expected Bayesian estimation for exponential model based on simple step stress with Type-I hybrid censored data[J]. Mathematical Biosciences and Engineering, 2022, 19(10): 9773-9791. doi: 10.3934/mbe.2022455

    Related Papers:

  • The procedure of selecting the values of hyper-parameters for prior distributions in Bayesian estimate has produced many problems and has drawn the attention of many authors, therefore the expected Bayesian (E-Bayesian) estimation method to overcome these problems. These approaches are used based on the step-stress acceleration model under the Exponential Type-I hybrid censored data in this study. The values of the distribution parameters are derived. To compare the E-Bayesian estimates to the other estimates, a comparative study was conducted using the simulation research. Four different loss functions are used to generate the Bayesian and E-Bayesian estimators. In addition, three alternative hyper-parameter distributions were used in E-Bayesian estimation. Finally, a real-world data example is examined for demonstration and comparative purposes.



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    [1] D. V. Lindley, A. F. Smith, Bayes estimates for the linear model, J. R. Stat. Soc. Ser. B, 34 (1972), 1–18. https://doi.org/10.1111/j.2517-6161.1972.tb00885.x doi: 10.1111/j.2517-6161.1972.tb00885.x
    [2] M. Han, E-Bayesian estimation and hierarchical Bayesian estimation of failure rate, Appl. Math. Model., 33 (2009), 1915–1922. https://doi.org/10.1016/j.apm.2008.03.019 doi: 10.1016/j.apm.2008.03.019
    [3] T. Ando, A. Zellner, Hierarchical Bayesian analysis of the seemingly unrelated regression and simultaneous equations models using a combination of direct Monte Carlo and importance sampling techniques, Bayesian Anal., 5 (2010), 65–5. https://doi.org/10.1214/10-BA503 doi: 10.1214/10-BA503
    [4] M. Han, The E-Bayesian and hierarchical Bayesian estimations of Pareto distribution parameter under different loss functions, J. Stat. Comput. Simul., 87 (2017), 577–593. https://doi.org/10.1080/00949655.2016.1221408 doi: 10.1080/00949655.2016.1221408
    [5] F. Kızılaslan, The E-Bayesian and hierarchical Bayesian estimations for the proportional reversed hazard rate model based on record values, J. Stat. Comput. Simul., 87 (2017), 2253–2273. https://doi.org/10.1080/00949655.2017.1326118 doi: 10.1080/00949655.2017.1326118
    [6] M. Han, Expected Bayesian method for forecast of security investment, J. Operat. Res. Manage. Sci., 14 (2005), 89–102. https://www.semanticscholar.org
    [7] M. Han, E-Bayesian method to estimate failure rate, in the sixth international symposium on operations research and its applications (ISOR06) Xinjiang, (2006), 299–311. http://180.76.190.102/LNOR/6/ISORA2006F25.pdf
    [8] Q. Yin, H. Liu, Bayesian estimation of geometric distribution parameter under the scaled squared error loss function, in 2010 the 2nd Conference on Environmental Science and Information Application Technology, 2 (2010), 650–653. https://doi.org/10.1109/ESIAT.2010.5568314
    [9] Z. F. Jaheen, H. M. Okasha, E-Bayesian estimation for the Burr type XII model based on type-2 censoring, J. Stat. Comput. Simul., 35 (2011), 4730–4737. https://doi.org/10.1016/j.apm.2011.03.055 doi: 10.1016/j.apm.2011.03.055
    [10] G. Cai, W. Xu, W. Zhang, P. Wang, Application of E-Bayes method in stock forecast, in 2011 Fourth International Conference on Information and Computing, (2011), 504–506. https://doi.org/10.1109/ICIC.2011.40
    [11] H. M. Okasha, E-Bayesian estimation of system reliability with Weibull distribution of components based on type-2 censoring, J. Adv. Res. Sci. Comput., 4 (2012), 34–45. http://www.i-asr.com/Journals/jarsc/
    [12] R. Azimi, F. Yaghmaei, B. Fasihi, E-Bayesian estimation based on generalized half Logistic progressive type-II censored data, Int. J. Adv. Math. Sci., 1 (2013), 56–63. https://doi.org/10.14419/ijams.v1i2.759 doi: 10.14419/ijams.v1i2.759
    [13] M. H. Degroot, Optimal Statistical Decision, 1970. https://doi.org/10.1002/0471729000
    [14] A. Rabie, J. Li, E-Bayesian estimation for Burr-X distribution based on generalized type-I hybrid censoring scheme, Am. J. Math. Manag. Sci., 39 (2020), 41–55. https://doi.org/10.1080/01966324.2019.1579123 doi: 10.1080/01966324.2019.1579123
    [15] A. Rabie, J. Li, E-Bayesian estimation based on Burr-X generalized Type-II hybrid censored data, Symmetry, 11 (2019), 626. https://doi.org/10.3390/sym11050626 doi: 10.3390/sym11050626
    [16] A. Rabie, J. Li, E-Bayesian estimation for Burr-X distribution based on Type-I hybrid censoring scheme, IAENG Int. J. Appl. Math., 48 (2018), 244–250.
    [17] A. Rabie, E-Bayesian estimation for a constant-stress partially accelerated life test based on Burr-X Type-I hybrid censored data J. Stat. Manage. Syst., 24 (2021), 1649–1667. https://doi.org/10.1080/09720510.2020.1842550
    [18] M. Nagy, K. S. Sultan, M. H. Abu-Moussa, Analysis of the generalized progressive hybrid censoring from Burr Type-XII lifetime model, J. Stat. Comput. Simul., 6 (2021), 9675–9704. https://www.aimspress.com/article/doi/10.3934/math.2021564
    [19] M. Nagy, A. F. Alrasheedi, The lifetime analysis of the Weibull model based on Generalized Type-I progressive hybrid censoring schemes, J. Stat. Comput. Simul., 19 (2022), 2330–2354. https://www.aimspress.com/article/doi/10.3934/mbe.2022108
    [20] M. Nagy, A. F. Alrasheedi, Estimations of generalized exponential distribution parameters based on Type I generalized progressive hybrid censored data, Comput. Math. Methods Med., 2022 (2022). https://doi.org/10.1155/2022/8058473
    [21] M. Nagy, A. F. Alrasheedi, Classical and Bayesian inference using Type-II unified progressive hybrid censored samples for pareto model Appl. Bionics. Biomech., 2022 (2022). https://doi.org/10.1155/2022/2073067
    [22] A. M. A. El-Raheem, M. Hosny, M. H. Abu-Moussa, On progressive censored competing risks data: real data application and simulation study, Mathematics, 9 (2021), 1805. https://doi.org/10.3390/math9151805 doi: 10.3390/math9151805
    [23] M. Nassar, H. Okasha, M. Albassam, E-Bayesian estimation and associated properties of simple step–stress model for exponential distribution based on type-II censoring, Qual. Reliab. Eng. Int., 37 (2021), 997–1016. https://doi.org/10.1002/qre.2778 doi: 10.1002/qre.2778
    [24] D. K. Bhaumik, K. Kapur, R. D. Gibbons, Testing parameters of a gamma distribution for small samples, Technometrics, 51 (2009), 326–334. https://doi.org/10.1198/tech.2009.07038 doi: 10.1198/tech.2009.07038
    [25] R. Shanker, F. Hagos, S. Sujatha, On modeling of Lifetimes data using exponential and Lindley distributions, Biom. Biostat. Int. J., 2 (2015), 1–9. https://doi.org/10.15406/bbij.2015.02.00042 doi: 10.15406/bbij.2015.02.00042
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