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Maximum-type tests for high-dimensional regression coefficients using Wilcoxon scores

机译:使用Wilcoxon分数的高维回归系数的最大型测试

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In this article, we develop new maximum-type tests to infer the overall significance of coefficients in high-dimensional linear models based on the Wilcoxon scores. The proposed testing procedures are free of error variance estimation and robust to heavy-tailed distributions and outliers, making them widely applicable in practice. We incorporate the dependence structure among predictors in the test statistics to enhance their powers. The limiting null distributions of the test statistics are derived to be the extreme value distribution of type I under regularity conditions. To reduce the size distortion, we further propose a multiplier bootstrap method based on the high-dimensional Gaussian approximations, which does not impose any structural assumptions on the unknown covariance matrices. We also evaluate the powers of proposed tests theoretically in comparison with two existing methods. The effectiveness of our proposed tests in the finite samples is illustrated through simulation studies and a real data application. (C) 2020 Elsevier B.V. All rights reserved.
机译:在本文中,我们开发了新的最大类型测试,以根据Wilcoxon分数推断高维线性模型中系数的总体显著性。所提出的测试方法不存在误差方差估计,对重尾分布和异常值具有鲁棒性,因此在实践中具有广泛的适用性。我们在测试统计中加入了预测因子之间的依赖结构,以增强它们的能力。在正则条件下,检验统计量的极限零分布被导出为I型极值分布。为了减少尺寸失真,我们进一步提出了一种基于高维高斯近似的乘法器自举方法,该方法不对未知协方差矩阵施加任何结构假设。我们还通过与两种现有方法的比较,从理论上评估了所提出测试的威力。通过模拟研究和实际数据应用,说明了我们提出的有限样本测试的有效性。(C) 2020爱思唯尔B.V.版权所有。

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