IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v51y2022i9p2689-2709.html
   My bibliography  Save this article

Estimation of population proportion using concomitant based ranked set sampling

Author

Listed:
  • Azhar Mehmood Abbasi
  • Muhammad Yousaf Shad

Abstract

This paper considers the concomitant based double ranked set sampling (CDRSS) for estimating the population proportion and compares with existing concomitant based ranked set sampling (CRSS) and simple random sampling (SRS) schemes. Moreover, taking into account information on a single concomitant variable, we also develop ratio-and exponential-type estimators, along with their biases and mean square errors (MSEs) up to first order of approximation, for precisely estimating the population proportion using CRSS and CDRSS schemes. The advantages of the ratio-and exponential-type estimators over SRS estimator are investigated in terms of the relative precision. A real data application is also given to support the theory.

Suggested Citation

  • Azhar Mehmood Abbasi & Muhammad Yousaf Shad, 2022. "Estimation of population proportion using concomitant based ranked set sampling," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(9), pages 2689-2709, March.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:9:p:2689-2709
    DOI: 10.1080/03610926.2021.1916529
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2021.1916529
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2021.1916529?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:51:y:2022:i:9:p:2689-2709. 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.