Novel multiplier bootstrap tests for high-dimensional data with applications to MANOVA
Novel multiplier bootstrap tests for high-dimensional data with applications to MANOVA
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DOI：
10.1016/j.csda.2022.107619
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Nilanjana Chakraborty;L. Sakhanenko
中科院分区：
文献类型：
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作者：
Nilanjana Chakraborty;L. Sakhanenko
针对高维均值的线性假设检验，提出了新的自举检验方法。特别是，他们处理多样本的单和双向MANOVA测试不相等的细胞大小和不相等的未知细胞协方差，以及对比测试在优雅和统一的方式。将新测试方法与现有的流行测试方法进行了理论比较和仿真研究。它们具有一致性、计算效率和非常温和的时刻/尾部条件。它们避免了相关性或精度矩阵的估计，并允许维度随样本量呈指数增长。此外，它们允许组的数量和稀疏度随样本量呈指数增长，从而扩大了它们的适用性。
New bootstrap tests are proposed for linear hypotheses testing of high-dimensional means. In particular, they handle multiple-sample one- and two-way MANOVA tests with unequal cell sizes and unequal unknown cell covariances, as well as contrast tests in elegant and unified way. New tests are compared theoretically and on simulations studies with existing popular contemporary tests. They enjoy consistency, computational efficiency, very mild moment/tail conditions. They avoid the estimation of correlation or precision matrices, and allow the dimension to grow with sample size exponentially. Additionally, they allow the number of groups and the sparsity to grow with the sample size exponentially, thus broadening their applicability.