IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v136y2017icp36-62.html
   My bibliography  Save this article

Classical and Bayesian estimation of reliability in a multicomponent stress–strength model based on the proportional reversed hazard rate mode

Author

Listed:
  • Kızılaslan, Fatih

Abstract

In this study, we consider a multicomponent system which has k statistically independent and identically distributed strength components X1,…,Xk and each component is exposed to a common random stress Y when the underlying distributions belonging to the proportional reversed hazard rate model. The system is regarded as operating only if at least s out of k(1≤s≤k) strength variables exceeds the random stress. The reliability of the system is estimated by using both frequentist and Bayesian approach. The Bayes estimates for the reliability of the system have been developed by using Lindley’s approximation and the Markov Chain Monte Carlo method due to the lack of explicit forms. The uniformly minimum variance unbiased and exact Bayes estimates for the reliability of the system are also obtained analytically when the common scale parameter is known. The asymptotic confidence interval and coverage probabilities are derived based on both the Fisher and the observed information matrices. The highest probability density credible interval is constructed by using Markov Chain Monte Carlo method. Monte Carlo simulations are performed to compare the performances of the proposed reliability estimators. Real data set is also analyzed for an illustration of the findings.

Suggested Citation

  • Kızılaslan, Fatih, 2017. "Classical and Bayesian estimation of reliability in a multicomponent stress–strength model based on the proportional reversed hazard rate mode," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 136(C), pages 36-62.
    • Handle: RePEc:eee:matcom:v:136:y:2017:i:c:p:36-62
    DOI: 10.1016/j.matcom.2016.10.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475416302063
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2016.10.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. EryIlmaz, Serkan, 2010. "On system reliability in stress-strength setup," Statistics & Probability Letters, Elsevier, vol. 80(9-10), pages 834-839, May.
    2. Mustafa Nadar & Alexander Papadopoulos & Fatih Kızılaslan, 2013. "Statistical analysis for Kumaraswamy’s distribution based on record data," Statistical Papers, Springer, vol. 54(2), pages 355-369, May.
    3. Wang, Bing Xing & Yu, Keming & Coolen, Frank P.A., 2015. "Interval estimation for proportional reversed hazard family based on lower record values," Statistics & Probability Letters, Elsevier, vol. 98(C), pages 115-122.
    4. Gupta, Ramesh C. & Ghitany, M.E. & Al-Mutairi, D.K., 2012. "Estimation of reliability in a parallel system with random sample size," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 83(C), pages 44-55.
    5. Eryilmaz, Serkan, 2008. "Multivariate stress-strength reliability model and its evaluation for coherent structures," Journal of Multivariate Analysis, Elsevier, vol. 99(9), pages 1878-1887, October.
    6. Debasis Kundu & Rameshwar D. Gupta, 2005. "Estimation of P[Y > X] for generalized exponential distribution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 61(3), pages 291-308, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Devendra Pratap Singh & Mayank Kumar Jha & Yogesh Mani Tripathi & Liang Wang, 2023. "Inference on a Multicomponent Stress-Strength Model Based on Unit-Burr III Distributions," Annals of Data Science, Springer, vol. 10(5), pages 1329-1359, October.
    2. M. S. Kotb & M. Z. Raqab, 2021. "Estimation of reliability for multi-component stress–strength model based on modified Weibull distribution," Statistical Papers, Springer, vol. 62(6), pages 2763-2797, December.
    3. Liang Wang & Huizhong Lin & Kambiz Ahmadi & Yuhlong Lio, 2021. "Estimation of Stress-Strength Reliability for Multicomponent System with Rayleigh Data," Energies, MDPI, vol. 14(23), pages 1-23, November.
    4. Yuhlong Lio & Tzong-Ru Tsai & Liang Wang & Ignacio Pascual Cecilio Tejada, 2022. "Inferences of the Multicomponent Stress–Strength Reliability for Burr XII Distributions," Mathematics, MDPI, vol. 10(14), pages 1-28, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fatih Kızılaslan, 2018. "Classical and Bayesian estimation of reliability in a multicomponent stress–strength model based on a general class of inverse exponentiated distributions," Statistical Papers, Springer, vol. 59(3), pages 1161-1192, September.
    2. Jana, Nabakumar & Bera, Samadrita, 2022. "Interval estimation of multicomponent stress–strength reliability based on inverse Weibull distribution," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 191(C), pages 95-119.
    3. Mustafa Nadar & Fatih Kızılaslan, 2014. "Classical and Bayesian estimation of $$P(X>Y)$$ P ( X > Y ) using upper record values from Kumaraswamy’s distribution," Statistical Papers, Springer, vol. 55(3), pages 751-783, August.
    4. Khan, Ruhul Ali, 2023. "Two-sample nonparametric test for proportional reversed hazards," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    5. Deepesh Bhati & Mohd. Malik & H. Vaman, 2015. "Lindley–Exponential distribution: properties and applications," METRON, Springer;Sapienza Università di Roma, vol. 73(3), pages 335-357, December.
    6. Y. L. Lio & Tzong-Ru Tsai, 2012. "Estimation of δ= P ( X > Y ) for Burr XII distribution based on the progressively first failure-censored samples," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 309-322, April.
    7. Christina Empacher & Udo Kamps & Grigoriy Volovskiy, 2023. "Statistical Prediction of Future Sports Records Based on Record Values," Stats, MDPI, vol. 6(1), pages 1-17, January.
    8. Wang, Bing Xing & Ye, Zhi-Sheng, 2015. "Inference on the Weibull distribution based on record values," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 26-36.
    9. Wong, Augustine C.M. & Wu, Yan Yan, 2009. "A note on interval estimation of P(X," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3650-3658, August.
    10. Liu, Yiming & Shi, Yimin & Bai, Xuchao & Zhan, Pei, 2018. "Reliability estimation of a N-M-cold-standby redundancy system in a multicomponent stress–strength model with generalized half-logistic distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 231-249.
    11. Ehsan Fayyazishishavan & Serpil Kılıç Depren, 2021. "Inference of stress-strength reliability for two-parameter of exponentiated Gumbel distribution based on lower record values," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-12, April.
    12. Eloísa Díaz-Francés & José Montoya, 2013. "The simplicity of likelihood based inferences for P(X > Y) and for the ratio of means in the exponential model," Statistical Papers, Springer, vol. 54(2), pages 499-522, May.
    13. A. Asgharzadeh & M. Kazemi & D. Kundu, 2017. "Estimation of $$P(X>Y)$$ P ( X > Y ) for Weibull distribution based on hybrid censored samples," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(1), pages 489-498, January.
    14. M. J. S. Khan & Bushra Khatoon, 2020. "Statistical Inferences of $$R=P(X," Annals of Data Science, Springer, vol. 7(3), pages 525-545, September.
    15. Abhimanyu Singh Yadav & S. K. Singh & Umesh Singh, 2019. "Bayesian estimation of $$R=P[Y," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 905-917, October.
    16. Farha Sultana & Yogesh Mani Tripathi & Shuo-Jye Wu & Tanmay Sen, 2022. "Inference for Kumaraswamy Distribution Based on Type I Progressive Hybrid Censoring," Annals of Data Science, Springer, vol. 9(6), pages 1283-1307, December.
    17. Jeongwook Lee & Joon Jin Song & Yongku Kim & Jung In Seo, 2020. "Estimation and Prediction of Record Values Using Pivotal Quantities and Copulas," Mathematics, MDPI, vol. 8(10), pages 1-16, October.
    18. Akram Kohansal, 2019. "On estimation of reliability in a multicomponent stress-strength model for a Kumaraswamy distribution based on progressively censored sample," Statistical Papers, Springer, vol. 60(6), pages 2185-2224, December.
    19. Lemonte, Artur J., 2013. "A new exponential-type distribution with constant, decreasing, increasing, upside-down bathtub and bathtub-shaped failure rate function," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 149-170.
    20. EryIlmaz, Serkan, 2010. "On system reliability in stress-strength setup," Statistics & Probability Letters, Elsevier, vol. 80(9-10), pages 834-839, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:matcom:v:136:y:2017:i:c:p:36-62. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.