论文标题
两样本的高维平均测试基于预科
Two-Sample High Dimensional Mean Test Based On Prepivots
论文作者
论文摘要
比较两个多变量随机变量时，平均向量的测试平等是非常常用的标准。当变量的数量大于样本量时，诸如Hotelling的T平方之类的传统测试将变得无法使用或输出小功率。在本文中，w} e提出了使用前卵和埃奇沃思（Edgeworth）扩展的测试，以测试“大p，小n”设置中两个群体平均向量的平等。得出了测试统计量的渐近无效分布，并表明当N和P在稀疏替代方案中增加到无穷大时，建议的测试的能力会收敛于某些替代方案。将提出的测试统计量的有限样本性能与旨在通过模拟处理“大P，小N”情况的其他最近开发的测试进行了比较。拟议的测试可以达到I型错误率和功率的竞争率。我们测试的有用性通过应用在两个微阵列基因表达数据集中说明
Testing equality of mean vectors is a very commonly used criterion when comparing two multivariate random variables. Traditional tests such as Hotelling's T-squared become either unusable or output small power when the number of variables is greater than the combined sample size. In this paper, w}e propose a test using both prepivoting and Edgeworth expansion for testing the equality of two population mean vectors in the "large p, small n" setting. The asymptotic null distribution of the test statistic is derived and it is shown that the power of suggested test converges to one under certain alternatives when both n and p increase to infinity against sparse alternatives. Finite sample performance of the proposed test statistic is compared with other recently developed tests designed to also handle the "large p, small n" situation through simulations. The proposed test achieves competitive rates for both type I error rate and power. The usefulness of our test is illustrated by applications to two microarray gene expression data sets
