高维数据的双样本检验及其在基因集检验中的应用
A two-sample test for high-dimensional data with applications to gene-set testing
摘要：
当数据维数远大于样本量时，我们提出了高维数据均值的双样本检验。Hotelling的经典$T^2$测试不适用于这种“大$p$，小$n$“的情况。所提出的检验不需要数据维度和样本量之间关系的明确条件。这为分析高维数据提供了很大的灵活性。所提出的测试的应用程序是在测试的显着性，我们证明在白血病数据集的实证研究的基因集。
英文摘要：
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.
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