Abstract In this paper, we discuss a two-way multivariate analysis of variance in high-dimensional settings. With a high-dimensional setting, we propose new approximate tests that work well under the following conditions: 1. The error vectors do not necessarily follow a multivariate normal distribution, 2. The cell sizes are unequal, 3. The cell covariance matrices are unequal, and 4. The dimension p is much larger than the total cell size n . The accuracy of the proposed tests with finite samples is shown through simulations for a variety of high-dimensional scenarios.

中文翻译：

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高维设置中具有不等单元大小和不等单元协方差矩阵的二维多元方差分析

摘要 在本文中，我们讨论了高维环境中方差的双向多变量分析。在高维设置下，我们提出了在以下条件下运行良好的新近似测试：1. 误差向量不一定遵循多元正态分布，2. 单元格大小不等，3. 单元格协方差矩阵不等, 和 4. 维度 p 远大于总像元大小 n 。通过对各种高维场景的模拟显示了所提出的有限样本测试的准确性。