IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-7908-2604-3_36.html
   My bibliography  Save this book chapter

Multiblock Method for Categorical Variables. Application to the Study of Antibiotic Resistance

In: Proceedings of COMPSTAT'2010

Author

Listed:
  • Stéphanie Bougeard

    (AFSSA (French Agency for Food Safety), Department of Epidemiology)

  • El Mostafa Qannari

    (ONIRIS (Nantes-Atlantic National College of Veterinary Medicine, Food Science and Engineering), Department of Sensometrics and Chemometrics)

  • Claire Chauvin

    (AFSSA (French Agency for Food Safety), Department of Epidemiology)

Abstract

We address the problem of describing several categorical variables with a prediction purpose. We focus on methods in the multiblock modelling framework, each block being formed of the indicator matrix associated with each qualitative variable.We propose a method, called categorical multiblock Redundancy Analysis, based on a well-identified global optimization criterion which leads to an eigensolution. In comparison with usual procedures, such as logistic regression, the method is well-adapted to the case of a large number of redundant explanatory variables. Practical uses of the proposed method are illustrated using an empirical example in the field of epidemiology.

Suggested Citation

  • Stéphanie Bougeard & El Mostafa Qannari & Claire Chauvin, 2010. "Multiblock Method for Categorical Variables. Application to the Study of Antibiotic Resistance," Springer Books, in: Yves Lechevallier & Gilbert Saporta (ed.), Proceedings of COMPSTAT'2010, pages 389-396, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2604-3_36
    DOI: 10.1007/978-3-7908-2604-3_36
    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:978-3-7908-2604-3_36. 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.