IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-981-10-3794-8_12.html
   My bibliography  Save this book chapter

Generalized Estimating Equation

In: Analysis of Repeated Measures Data

Author

Listed:
  • M. Ataharul Islam

    (University of Dhaka, Institute of Statistical Research and Training (ISRT))

  • Rafiqul I. Chowdhury

    (University of Dhaka, Institute of Statistical Research and Training (ISRT))

Abstract

The generalized estimating equation (GEE) uses a quasi-likelihood approach for analyzing data with correlated outcomes. This is an extension of GLM and uses quasi-likelihood method for cluster or repeated outcomes. If observations on outcome variable are repeated, it is likely that the observations are correlated. In addition, non-normality of outcome variables is a common phenomenon in real-life problems. In such situations, use of quasi-likelihood estimating equations provides necessary methodological support for estimating parameters of a regression model. The GEE is a marginal model approach for analyzing repeated measures data developed by Zeger and Liang (1986) and Liang and Zeger (1986). This approach can be considered as a semiparametric approach because it does not require full specification of the underlying joint probability distribution for repeated outcome variables rather assumes likelihood for marginal distribution and a working correlation matrix. The correlation matrix represents the correlation between observations in clusters observed from panel, longitudinal, or family studies. In this chapter, an overview of GEE is presented.

Suggested Citation

  • M. Ataharul Islam & Rafiqul I. Chowdhury, 2017. "Generalized Estimating Equation," Springer Books, in: Analysis of Repeated Measures Data, chapter 0, pages 161-167, Springer.
    • Handle: RePEc:spr:sprchp:978-981-10-3794-8_12
    DOI: 10.1007/978-981-10-3794-8_12
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:spr:sprchp:978-981-10-3794-8_12. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.