A two-sample test for high-dimensional data with applications to gene-set testing

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.

• Publication year

  2010

• Venue

  Annals of Statistics

• Publication date

  2010-02-24

• Fields of study

  Biology, Mathematics

• Identifiers
  DOI 10.1214/09-AOS716arXiv 1002.4547
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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