IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v40y2013i8p1639-1658.html
   My bibliography  Save this article

Selection of suitable prior for the Bayesian mixture of a class of lifetime distributions under type-I censored datasets

Author

Listed:
  • Syed Mohsin Ali Kazmi
  • Muhammad Aslam
  • Sajid Ali
  • Nasir Abbas

Abstract

This paper explores the study on mixture of a class of probability density functions under type-I censoring scheme. In this paper, we mold a heterogeneous population by means of a two-component mixture of the class of probability density functions. The parameters of the class of mixture density functions are estimated and compared using the Bayes estimates under the squared-error and precautionary loss functions. A censored mixture dataset is simulated by probabilistic mixing for the computational purpose considering particular case of the Maxwell distribution. Closed-form expressions for the Bayes estimators along with their posterior risks are derived for censored as well as complete samples. Some stimulating comparisons and properties of the estimates are presented here. A factual dataset has also been for illustration.

Suggested Citation

  • Syed Mohsin Ali Kazmi & Muhammad Aslam & Sajid Ali & Nasir Abbas, 2013. "Selection of suitable prior for the Bayesian mixture of a class of lifetime distributions under type-I censored datasets," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1639-1658, August.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1639-1658
    DOI: 10.1080/02664763.2013.789831
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2013.789831
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2013.789831?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.

    Citations

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


    Cited by:

    1. Sajid Ali & Muhammad Riaz, 2014. "On the generalized process capability under simple and mixture models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(4), pages 832-852, April.
    2. Jafer Rahman & Muhammad Aslam, 2017. "On estimation of two-component mixture inverse Lomax model via Bayesian approach," 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 99-109, January.

    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:40:y:2013:i:8:p:1639-1658. 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: 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.