Comparison of Discrimination Methods for High Dimensional Data
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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|Date of creation:||Mar 2005|
|Date of revision:|
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