IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v17y2002i2d10.1007_s001800200100.html
   My bibliography  Save this article

Multivariate permutation tests for the k-sample problem with clustered data

Author

Listed:
  • Jörg Rahnenführer

    (Heinrich-Heine-Universität Düsseldorf)

Abstract

Summary We investigate the optimal choice of clustering algorithms for multivariate data sets. We make use of algorithms that define partitions by maximal support planes (MSP) of a convex function and have been profoundly investigated by Pötzelberger and Strasser (2000). This is a wide range class containing as special cases both the well known k-means algorithm and the Kohonen (1985) algorithm. We compare the quality of the clustering procedures by first applying them to multivariate data sets and then treating a k-sample problem. For computing the test statistics the data points are replaced by their conditional expectations with respect to the MSP-partition. Monte Carlo simulations of power functions for tests that are carried out as multivariate permutation tests show a vital and decisive connection between the optimal choice of the algorithm and the tails of the probability distribution of the data. Especially for distributions with heavy tails the performance of k-means type algorithms totally breaks down.

Suggested Citation

  • Jörg Rahnenführer, 2002. "Multivariate permutation tests for the k-sample problem with clustered data," Computational Statistics, Springer, vol. 17(2), pages 165-184, July.
    • Handle: RePEc:spr:compst:v:17:y:2002:i:2:d:10.1007_s001800200100
    DOI: 10.1007/s001800200100
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s001800200100
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s001800200100?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.

    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:compst:v:17:y:2002:i:2:d:10.1007_s001800200100. 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.