|One important application of gene expression microarray data is
classification of samples into categories, such as types of tumor.
Gene selection procedures become crucial since gene expression
data from DNA microarrays are characterized by thousands measured
genes on only a few subjects. Not all these genes are thought to
determine a specific genetic trait. In this dissertation, I
develop a novel nonparametric procedure for selecting such genes.
This rank-based forward selection procedure rewards genes for
their contribution towards determining the trait but penalizes
them for their similarity to genes that are already selected. I
will show that my method gives lower misclassification error rates
than the dimension reduction methods such as principal component
analysis and partial least square analysis. I also explore more
properties of Wilcoxon-Mann-Whitney (WMW) statistic and propose a
new classifier based on WMW to reduce the misclassification error
rate. Real data analysis and Monte Carlo simulation demonstrate
the superiority of the proposed methods to the classical methods
in several situations.
|Mathematics and Statistics
|Nonparametric Methods for Classification and Related Feature Selection Procedures