A Two Sample Test for High Dimensional Data with Applications to Gene-set Testing

Song Chen and Yingli Qin

MPRA Paper from University Library of Munich, Germany

Abstract: We proposed a two sample test for means of high dimensional data when the data dimension is much larger than the sample size. The classical Hotelling's $T^2$ test does not work for this ``large p, small n" situation. The proposed test does not require explicit conditions on 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: large p small n; martingale central limit theorem; multiple comparison. (search for similar items in EconPapers)
JEL-codes: C1 C12 C14 (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (118)

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