IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v56y2007i2p223-234.html
   My bibliography  Save this article

Psychotropic drug classification based on sleep–wake behaviour of rats

Author

Listed:
  • Kristien Wouters
  • Abdellah Ahnaou
  • Jose Cortinas Abrahantes
  • Geert Molenberghs
  • Helena Geys
  • Luc Bijnens
  • Wilhelmus H. I. M. Drinkenburg

Abstract

Summary. The aim of the paper is to present methodology for the classification of potential psychotropic drugs on the basis of their activity. We first sketch the background of this class of drugs and then zoom in on so‐called pharmacoelectroencephalogram studies. These data pose some statistical challenges. For classification purposes, we propose a flexible hierarchical discriminant analysis tool, allowing us to take the specific nature of the drug class into account, as well as the features of the mixed models, in combination with fractional polynomials, fitted to the electroencephalogram data. The method is evaluated against the background of existing methods. The method's performance is studied by using a comprehensive analysis of a large electroencephalogram data set.

Suggested Citation

  • Kristien Wouters & Abdellah Ahnaou & Jose Cortinas Abrahantes & Geert Molenberghs & Helena Geys & Luc Bijnens & Wilhelmus H. I. M. Drinkenburg, 2007. "Psychotropic drug classification based on sleep–wake behaviour of rats," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(2), pages 223-234, March.
    • Handle: RePEc:bla:jorssc:v:56:y:2007:i:2:p:223-234
    DOI: 10.1111/j.1467-9876.2007.00575.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9876.2007.00575.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9876.2007.00575.x?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
    ---><---

    Citations

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


    Cited by:

    1. Ana Arribas-Gil & Rolando De la Cruz & Emilie Lebarbier & Cristian Meza, 2015. "Classification of longitudinal data through a semiparametric mixed-effects model based on lasso-type estimators," Biometrics, The International Biometric Society, vol. 71(2), pages 333-343, June.
    2. Luts, Jan & Molenberghs, Geert & Verbeke, Geert & Van Huffel, Sabine & Suykens, Johan A.K., 2012. "A mixed effects least squares support vector machine model for classification of longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 611-628.

    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:bla:jorssc:v:56:y:2007:i:2:p:223-234. 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: Wiley-Blackwell Digital Licensing or Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.