IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v46y2017i24p12045-12058.html
   My bibliography  Save this article

Estimation for the scaled half-logistic distribution under Type-I progressively hybrid censoring scheme

Author

Listed:
  • Chao Wang
  • Hong Liu

Abstract

This paper addresses the estimation for the unknown scale parameter of the half-logistic distribution based on a Type-I progressively hybrid censoring scheme. We evaluate the maximum likelihood estimate (MLE) via numerical method, and EM algorithm, and also the approximate maximum likelihood estimate (AMLE). We use a modified acceptance rejection method to obtain the Bayes estimate and corresponding highest posterior confidence intervals. We perform Monte Carlo simulations to compare the performances of the different methods, and we analyze one dataset for illustrative purposes.

Suggested Citation

  • Chao Wang & Hong Liu, 2017. "Estimation for the scaled half-logistic distribution under Type-I progressively hybrid censoring scheme," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(24), pages 12045-12058, December.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:24:p:12045-12058
    DOI: 10.1080/03610926.2017.1291968
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2017.1291968
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2017.1291968?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:46:y:2017:i:24:p:12045-12058. 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.