New parametric and nonparametric multiple test procedures for high-dimensional data

  • Siegfried Kropf Otto von Guericke University Magdeburg
  • Gerhard Hommel Johannes Gutenberg University Mainz
Keywords: multiple tests, familywise error rate, sequential procedure, parametric tests, nonparametric tests, microarrays


Modern techniques in biomedical research as microarrays or computer based imaging techniques often yield extremely high-dimensional data for a patient. We propose several procedures for separate tests with all variables controlling the experimentwise type I error in a parametric as well as in a nonparametric setup. These procedures utilise the idea that all variables should have a similar scale. Otherwise the procedures are less powerful but the type I error is still under strong control.
Various modifications of the basic procedures weaken the power-dependence on the assumption of equal variances. All procedures are very simple to implement. They are demonstrated here in a microarray data set, comparing their performance with standard techniques.


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Author Biographies

Siegfried Kropf, Otto von Guericke University Magdeburg

Institute of Biometry and Medical Informatics

Gerhard Hommel, Johannes Gutenberg University Mainz

Institute of Medical Biometry, Epidemiology and Informatics