A TWO-SAMPLE TEST FOR HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO GENE-SET TESTING

• 成果类型: Article
• 署名作者: Chen, Song Xi; Qin, Ying-Li
• 署名单位: Iowa State University; Peking University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/09-AOS716
• 发表日期: 2010
• 页码: 808-835
• 关键词: false discovery rate; microarray data; COVARIANCE-MATRIX; hypothesis tests; normalization; Consistency; CATEGORIES; expression; limit; MODEL

摘要

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 T(2) test does not work for this large p, small n 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.