Comparison of Discrimination Methods for High Dimensional Data
2007 年 37 巻 1 号 p. 123-134
詳細
2007 年 37 巻 1 号 p. 123-134
In microarray experiments, the dimension p of the data is very large but there are only a few observations N on the subjects/patients. In this article, the problem of classifying a subject into one of two groups, when p is large, is considered. Three procedures based on the 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.
