IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v45y2018i3p474-486.html
   My bibliography  Save this article

An improved meta-analysis for analyzing cylindrical-type time series data with applications to forecasting problem in environmental study

Author

Listed:
  • Shuo Wang
  • Sungsu Kim
  • Thelge Buddika Peiris

Abstract

In this paper, we propose an improved generalized least square (GLS) meta-analysis in a linear-circular regression, and show its utility in the analysis of a certain environmental issue. The existing GLS meta-analysis proposed in Becker and Wu has a serious flaw since information about the covariance among coefficients across studies is not utilized. In our proposed meta-analysis, we take the correlations between adjacent studies into account, and improve the existing GLS meta-analysis. We provide numerical examples to compare the proposed method with several other existing methods by using Akaike's Information Criterion, Bayesian Information Criterion and mean square prediction errors with applications to forecasting problem in Environmental study.

Suggested Citation

  • Shuo Wang & Sungsu Kim & Thelge Buddika Peiris, 2018. "An improved meta-analysis for analyzing cylindrical-type time series data with applications to forecasting problem in environmental study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(3), pages 474-486, February.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:3:p:474-486
    DOI: 10.1080/02664763.2017.1280451
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2017.1280451
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2017.1280451?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:japsta:v:45:y:2018:i:3:p:474-486. 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/CJAS20 .

    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.