IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v50y2021i12p2723-2731.html
   My bibliography  Save this article

Meta analysis of regression: a review and new approach with application to linear-circular regression model

Author

Listed:
  • Sungsu Kim
  • Thelge Buddika Peiris

Abstract

In a usual meta analysis of regression, it is assumed that co-variance among studies are zero. However, the main utility of a meta analysis is to provide an estimated overall effect by combining the results from related small studies. Therefore, incorporating co-variance among those small studies is essential, and in our earlier work, it was shown to improve the estimates obtained from the proposed generalized least square approach. In this paper, we provide a review of meta analysis of regression, then present an improved weighed least square approach to meta analysis that takes account into an appropriate co-variance structure among related studies. We illustrate our proposed method in a linear-circular regression setting with application to forecasting problem in Environmental Sciences, and compare the different approaches presented in this paper using the mean square prediction error (MSPE).

Suggested Citation

  • Sungsu Kim & Thelge Buddika Peiris, 2021. "Meta analysis of regression: a review and new approach with application to linear-circular regression model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(12), pages 2723-2731, June.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:12:p:2723-2731
    DOI: 10.1080/03610926.2019.1679183
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2019.1679183
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2019.1679183?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jiameng Hu & Baoying Ye & Zhongke Bai & Jiawei Hui, 2022. "Comparison of the Vegetation Index of Reclamation Mining Areas Calculated by Multi-Source Remote Sensing Data," Land, MDPI, vol. 11(3), pages 1-16, February.

    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:12:p:2723-2731. 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.