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• W2081874429 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 $T^2$ 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." @default.
• W2081874429 created "2016-06-24" @default.
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• W2081874429 date "2010-04-01" @default.
• W2081874429 modified "2023-10-11" @default.
• W2081874429 title "A two-sample test for high-dimensional data with applications to gene-set testing" @default.
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• W2081874429 doi "https://doi.org/10.1214/09-aos716" @default.
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