IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-642-57338-5_9.html
   My bibliography  Save this book chapter

Robust Inference Based on Quasi-likelihoods for Generalized Linear Models and Longitudinal Data

In: Developments in Robust Statistics

Author

Listed:
  • E. Cantoni

    (University of Geneva, Econometrics Department)

Abstract

Summary In this paper we introduce and develop robust versions of quasi-likelihood functions for model selection via an analysis-of-deviance type of procedure in generalized linear models and longitudinal data analysis. These robust functions are built upon natural classes of robust estimators and can be seen as weighted versions of their classical counterparts. The asymptotic theory of these test statistics is studied and their robustness properties are assessed for both generalized linear models and longitudinal data analysis. The proposed class of test statistics yields reliable inference even under model contamination. The analysis of a real data set completes the article.

Suggested Citation

  • E. Cantoni, 2003. "Robust Inference Based on Quasi-likelihoods for Generalized Linear Models and Longitudinal Data," Springer Books, in: Rudolf Dutter & Peter Filzmoser & Ursula Gather & Peter J. Rousseeuw (ed.), Developments in Robust Statistics, pages 114-124, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-57338-5_9
    DOI: 10.1007/978-3-642-57338-5_9
    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

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:978-3-642-57338-5_9. 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.