论文标题
在高斯假设下对FWER的渐近控制：应用于相关测试
Asymptotic control of FWER under Gaussian assumption: application to correlation tests
论文作者
论文摘要
在许多应用中，假设检验基于统计的渐近分布。本文的目的是阐明和扩展统计数据渐近高斯时的多个校正程序。我们提出了一个统一的框架来证明其渐近行为，这在高度相关的测试中是有效的。我们专注于提出多个测试统计数据的相关测试。所有这些关于相关性的多个多个测试程序均显示用于控制FWER。一项关于基于相关图估计的广泛模拟研究突出了有限的样本行为，对图的稀疏性的独立性以及对相关值的依赖性。权力的经验评估提供了所提出方法的比较。最终，对我们的程序进行了验证，提出了fMRI测量的大鼠大脑连接性的实际数据集。我们通过将我们的程序应用于与死大鼠的数据的完整零假设上，以确认我们的理论发现。关于活大鼠的数据显示了提出的程序的性能，以正确识别具有控制错误的大脑连接图。
In many applications, hypothesis testing is based on an asymptotic distribution of statistics. The aim of this paper is to clarify and extend multiple correction procedures when the statistics are asymptotically Gaussian. We propose a unified framework to prove their asymptotic behavior which is valid in the case of highly correlated tests. We focus on correlation tests where several test statistics are proposed. All these multiple testing procedures on correlations are shown to control FWER. An extensive simulation study on correlation-based graph estimation highlights finite sample behavior, independence on the sparsity of graphs and dependence on the values of correlations. Empirical evaluation of power provides comparisons of the proposed methods. Finally validation of our procedures is proposed on real dataset of rats brain connectivity measured by fMRI. We confirm our theoretical findings by applying our procedures on a full null hypotheses with data from dead rats. Data on alive rats show the performance of the proposed procedures to correctly identify brain connectivity graphs with controlled errors.
