IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0255944.html
   My bibliography  Save this article

Improved objective Bayesian estimator for a PLP model hierarchically represented subject to competing risks under minimal repair regime

Author

Listed:
  • Francisco Louzada
  • José A Cuminato
  • Oscar M H Rodriguez
  • Vera L D Tomazella
  • Paulo H Ferreira
  • Pedro L Ramos
  • Eder A Milani
  • Gustavo Bochio
  • Ivan C Perissini
  • Oilson A Gonzatto Junior
  • Alex L Mota
  • Luis F A Alegría
  • Danilo Colombo
  • Eduardo A Perondi
  • André V Wentz
  • Anselmo L Silva Júnior
  • Dante A C Barone
  • Hugo F L Santos
  • Marcus V C Magalhães

Abstract

In this paper, we propose a hierarchical statistical model for a single repairable system subject to several failure modes (competing risks). The paper describes how complex engineered systems may be modelled hierarchically by use of Bayesian methods. It is also assumed that repairs are minimal and each failure mode has a power-law intensity. Our proposed model generalizes another one already presented in the literature and continues the study initiated by us in another published paper. Some properties of the new model are discussed. We conduct statistical inference under an objective Bayesian framework. A simulation study is carried out to investigate the efficiency of the proposed methods. Finally, our methodology is illustrated by two practical situations currently addressed in a project under development arising from a partnership between Petrobras and six research institutes.

Suggested Citation

  • Francisco Louzada & José A Cuminato & Oscar M H Rodriguez & Vera L D Tomazella & Paulo H Ferreira & Pedro L Ramos & Eder A Milani & Gustavo Bochio & Ivan C Perissini & Oilson A Gonzatto Junior & Alex , 2021. "Improved objective Bayesian estimator for a PLP model hierarchically represented subject to competing risks under minimal repair regime," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-25, August.
  • Handle: RePEc:plo:pone00:0255944
    DOI: 10.1371/journal.pone.0255944
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255944
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0255944&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0255944?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
    ---><---

    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:plo:pone00:0255944. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.