IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v53y2024i11p3973-3991.html
   My bibliography  Save this article

Meta-analysis of exponential lifetime data from Type-I hybrid censored samples

Author

Listed:
  • Kiran Prajapat
  • Shuvashree Mondal
  • Sharmishtha Mitra
  • Debasis Kundu

Abstract

In this study, in order to do a life testing experiment, sampled units are divided into a prefixed number of groups with equal number of units. Units in all the groups are tested simultaneously and independently and, in each group the experiment is terminated as soon as a prefixed time elapses or a prefixed number of failures occurs. We provide the meta-analysis of an exponential lifetime data from Type-I hybrid censored samples. The main goal of this study is to obtain optimal schemes based on some optimality criteria by minimizing certain cost function that is based on a maximum likelihood estimator of mean lifetime. We provide the maximum likelihood estimator of mean lifetime and its probability density function under this set-up. Various optimal schemes have been provided by minimizing expected total cost incurred during the experiment as the raw moments can be obtained explicitly. Numerical results on bias and mean squared error of the maximum likelihood estimator have been reported. We also provide confidence intervals of the unknown parameter. For illustration, meta-analysis for a real data set of three groups is presented.

Suggested Citation

  • Kiran Prajapat & Shuvashree Mondal & Sharmishtha Mitra & Debasis Kundu, 2024. "Meta-analysis of exponential lifetime data from Type-I hybrid censored samples," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 53(11), pages 3973-3991, June.
  • Handle: RePEc:taf:lstaxx:v:53:y:2024:i:11:p:3973-3991
    DOI: 10.1080/03610926.2023.2169048
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2023.2169048
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/03610926.2023.2169048?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.

    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:lstaxx:v:53:y:2024:i:11:p:3973-3991. 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/lsta .

    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.