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

被引：443

作者：

Chen, Song Xi ^{[1
,2
]}

Qin, Ying-Li ^{[1
]}

机构：

[1] Iowa State Univ, Dept Stat, Ames, IA 50011 USA

[2] Peking Univ, Guanghua Sch Management, Beijing 100871, Peoples R China

来源：

ANNALS OF STATISTICS | 2010年 / 38卷 / 02期

关键词：

High dimension; gene-set testing; large p small n; martingale central limit theorem; multiple comparison; FALSE DISCOVERY RATE; MICROARRAY DATA; COVARIANCE-MATRIX; HYPOTHESIS TESTS; NORMALIZATION; CONSISTENCY; CATEGORIES; EXPRESSION; LIMIT; MODEL;

D O I：

10.1214/09-AOS716

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

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.

引用

页码：808 / 835

页数：28

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