A two-sample test for high-dimensional data with applications to gene-set testing
Statistics Theory
2010-02-25 v1 Statistics Theory
Abstract
We propose a two-sample test for the means of high-dimensional data when the data dimension is much larger than the sample size. Hotelling's classical test does not work for this "large , small " situation. The proposed test does not require explicit conditions in the relationship between the data dimension and sample size. This offers much flexibility in analyzing high-dimensional data. An application of the proposed test is in testing significance for sets of genes which we demonstrate in an empirical study on a leukemia data set.
Keywords
Cite
@article{arxiv.1002.4547,
title = {A two-sample test for high-dimensional data with applications to gene-set testing},
author = {Song Xi Chen and Ying-Li Qin},
journal= {arXiv preprint arXiv:1002.4547},
year = {2010}
}
Comments
Published in at http://dx.doi.org/10.1214/09-AOS716 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)