IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v50y2021i20p4685-4691.html
   My bibliography  Save this article

Ratio-type estimators for improving mean estimation using Poisson regression method

Author

Listed:
  • Haydar Koç

Abstract

Poisson regression model is the most common method used to model count response in many studies. This paper proposes a new ratio-type estimators based on Poisson regression in simple random sampling. The mean square error (MSE) equation of these estimators is obtained in this study. Theoretically, the MSE of the proposed estimators and the MSE of the traditional ratio estimators are compared. As a result of these comparisons, it has shown that the suggested estimator is more efficient than the traditional estimators. In addition, the results of the application part supported the theoretical findings of the study.

Suggested Citation

  • Haydar Koç, 2021. "Ratio-type estimators for improving mean estimation using Poisson regression method," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(20), pages 4685-4691, September.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:20:p:4685-4691
    DOI: 10.1080/03610926.2020.1777307
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2020.1777307
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2020.1777307?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:50:y:2021:i:20:p:4685-4691. 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.