IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v43y2016i15p2697-2712.html
   My bibliography  Save this article

Bayes estimation for exponential distributions with common location parameter and applications to multi-state reliability models

Author

Listed:
  • Nabakumar Jana
  • Somesh Kumar
  • Kashinath Chatterjee

Abstract

This paper considers the estimation of the stress–strength reliability of a multi-state component or of a multi-state system where its states depend on the ratio of the strength and stress variables through a kernel function. The article presents a Bayesian approach assuming the stress and strength as exponentially distributed with a common location parameter but different scale parameters. We show that the limits of the Bayes estimators of both location and scale parameters under suitable priors are the maximum likelihood estimators as given by Ghosh and Razmpour [15]. We use the Bayes estimators to determine the multi-state stress–strength reliability of a system having states between 0 and 1. We derive the uniformly minimum variance unbiased estimators of the reliability function. Interval estimation using the bootstrap method is also considered. Under the squared error loss function and linex loss function, risk comparison of the reliability estimators is carried out using extensive simulations.

Suggested Citation

  • Nabakumar Jana & Somesh Kumar & Kashinath Chatterjee, 2016. "Bayes estimation for exponential distributions with common location parameter and applications to multi-state reliability models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(15), pages 2697-2712, November.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:15:p:2697-2712
    DOI: 10.1080/02664763.2016.1142950
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2016.1142950
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2016.1142950?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. Alessandro Barbiero, 2012. "Interval estimators for reliability: the bivariate normal case," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(3), pages 501-512, 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. Zheng Liu & Xin Liu & Hong-Zhong Huang & Pingyu Zhu & Zhongwei Liang, 2022. "A new inherent reliability modeling and analysis method based on imprecise Dirichlet model for machine tool spindle," Annals of Operations Research, Springer, vol. 311(1), pages 295-310, April.
    2. Shuang Gu & Keping Li, 2019. "Reliability analysis of high-speed railway network," Journal of Risk and Reliability, , vol. 233(6), pages 1060-1073, December.
    3. Modibbo, Umar Muhammad & Arshad, Mohd. & Abdalghani, Omer & Ali, Irfan, 2021. "Optimization and estimation in system reliability allocation problem," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    4. Nabakumar Jana & Samadrita Bera, 2024. "Estimation of multicomponent system reliability for inverse Weibull distribution using survival signature," Statistical Papers, Springer, vol. 65(8), pages 5077-5108, October.
    5. Malekzadeh, Ahad & Kharrati-Kopaei, Mahmood, 2017. "An exact method for making inferences on the common location parameter of several heterogeneous exponential populations: Complete and censored data," Statistics & Probability Letters, Elsevier, vol. 129(C), pages 210-215.

    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. Joby K. Jose & M. Drisya, 2020. "Time-dependent stress–strength reliability models based on phase type distribution," Computational Statistics, Springer, vol. 35(3), pages 1345-1371, September.

    More about this item

    Statistics

    Access and download statistics

    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:taf:japsta:v:43:y:2016:i:15:p:2697-2712. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.