IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i1d10.1007_s00180-024-01497-y.html
   My bibliography  Save this article

Advancements in reliability estimation for the exponentiated Pareto distribution: a comparison of classical and Bayesian methods with lower record values

Author

Listed:
  • Shubham Saini

    (Graphic Era Hill University)

Abstract

Estimating the reliability of multicomponent systems is crucial in various engineering and reliability analysis applications. This paper investigates the multicomponent stress strength reliability estimation using lower record values, specifically for the exponentiated Pareto distribution. We compare classical estimation techniques, such as maximum likelihood estimation, with Bayesian estimation methods. Under Bayesian estimation, we employ Markov Chain Monte Carlo techniques and Tierney–Kadane’s approximation to obtain the posterior distribution of the reliability parameter. To evaluate the performance of the proposed estimation approaches, we conduct a comprehensive simulation study, considering various system configurations and sample sizes. Additionally, we analyze real data to illustrate the practical applicability of our methods. The proposed methodologies provide valuable insights for engineers and reliability analysts in accurately assessing the reliability of multicomponent systems using lower record values.

Suggested Citation

  • Shubham Saini, 2025. "Advancements in reliability estimation for the exponentiated Pareto distribution: a comparison of classical and Bayesian methods with lower record values," Computational Statistics, Springer, vol. 40(1), pages 353-382, January.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:1:d:10.1007_s00180-024-01497-y
    DOI: 10.1007/s00180-024-01497-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01497-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-024-01497-y?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.

    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:spr:compst:v:40:y:2025:i:1:d:10.1007_s00180-024-01497-y. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.