High-dimensional sparse MANOVA
High-dimensional sparse MANOVA
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DOI：
10.1016/j.jmva.2014.07.002
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发表时间：
2014-10
期刊：
影响因子：
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通讯作者：
T. Cai;Yin Xia
中科院分区：
文献类型：
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作者：
T. Cai;Yin Xia
本文研究了相依条件下多个高维均值向量的相等性检验问题。我们提出了一个测试，是基于线性变换的数据的精度矩阵，其中包括变量的依赖结构。导出了检验统计量的极限零分布，并证明其为I型极值分布。然而，当组的数目相对较大时，向极限分布的收敛是缓慢的。引入了一个中间校正因子，显著提高了测试的准确性。它表明，测试是特别强大的稀疏的替代品，并享有一定的最优性。进行了模拟研究，以检查的数值性能的测试，并与文献中给出的其他测试进行比较。数值结果表明，所提出的测试显着优于稀疏的替代品的测试。
This paper considers testing the equality of multiple high-dimensional mean vectors under dependency. We propose a test that is based on a linear transformation of the data by the precision matrix which incorporates the dependence structure of the variables. The limiting null distribution of the test statistic is derived and is shown to be the extreme value distribution of type I. The convergence to the limiting distribution is, however, slow when the number of groups is relatively large. An intermediate correction factor is introduced which significantly improves the accuracy of the test. It is shown that the test is particularly powerful against sparse alternatives and enjoys certain optimality. A simulation study is carried out to examine the numerical performance of the test and compare with other tests given in the literature. The numerical results show that the proposed test significantly outperforms those tests against sparse alternatives.