IDEAS home Printed from https://ideas.repec.org/a/taf/mpopst/v27y2020i4p216-231.html
   My bibliography  Save this article

Improved chain-ratio type estimator for population total in double sampling

Author

Listed:
  • Saurav Guha
  • Hukum Chandra

Abstract

Chain-ratio estimators are often used to improve the efficiency of the estimation of the population total or the mean using two auxiliary variables, available in two different phases. An improved chain-ratio estimator for the population total based on double sampling is proposed when auxiliary information is available for the first variable and not available for the second variable. The bias and the mean square error of this estimator are obtained for a large sample. Empirical evaluations using both model-based and design-based simulations show that the proposed estimator performs better than the ratio, the regression, and the difference estimators.

Suggested Citation

  • Saurav Guha & Hukum Chandra, 2020. "Improved chain-ratio type estimator for population total in double sampling," Mathematical Population Studies, Taylor & Francis Journals, vol. 27(4), pages 216-231, October.
    • Handle: RePEc:taf:mpopst:v:27:y:2020:i:4:p:216-231
    DOI: 10.1080/08898480.2019.1626635
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/08898480.2019.1626635
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/08898480.2019.1626635?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:mpopst:v:27:y:2020:i:4:p:216-231. 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/GMPS20 .

    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.