IDEAS home Printed from https://ideas.repec.org/a/ids/ijmore/v22y2022i1p74-92.html
   My bibliography  Save this article

Efficient classes of estimators for population variance using attribute

Author

Listed:
  • Shashi Bhushan
  • Anoop Kumar
  • Sumit Kumar

Abstract

This article deals with the problem of estimating population variance of study variable by using information on auxiliary attribute in simple random sampling. We have adapted the procedure of Kadilar and Cingi (2006) and established some efficient classes of estimators for population variance. The performance of the proposed estimators have been assessed by an empirical study using two real populations and the results demonstrate that the proposed estimators present an extensively greater efficiency when compared with the usual mean estimator, classical ratio, regression and exponential estimators suggested by Singh and Kumar (2011), Singh and Malik (2014) estimators, Zaman and Kadilar (2019) type estimators, Zaman (2020) type estimator and Cekim and Kadilar (2020) type estimator.

Suggested Citation

  • Shashi Bhushan & Anoop Kumar & Sumit Kumar, 2022. "Efficient classes of estimators for population variance using attribute," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 22(1), pages 74-92.
    • Handle: RePEc:ids:ijmore:v:22:y:2022:i:1:p:74-92
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=123124
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijmore:v:22:y:2022:i:1:p:74-92. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=320 .

    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.