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Comparison of Discrimination Methods for High Dimensional Data

  • M. S. Srivastava

    (Department of Statistics, University of Toronto)

  • Tatsuya Kubokawa

    (Faculty of Economics, University of Tokyo)

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    In microarray experiments, the dimension p of the data is very large but there are only few observations N on the subjects/patients. In this article, the problem of classifying a subject into one of the two groups, when p is large, is considered. Three procedures based on Moore-Penrose inverse of the sample covariance matrix and an empirical Bayes estimate of the precision matrix are proposed and compared with the DLDA procedure.

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    Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-324.

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    Length: 17 pages
    Date of creation: Mar 2005
    Date of revision:
    Handle: RePEc:tky:fseres:2005cf324
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