Rapid Acceleration of the Permutation Test via Transpositions.
Rapid Acceleration of the Permutation Test via Transpositions.
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DOI：
10.1007/978-3-030-32391-2_5
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发表时间：
2019-10
期刊：
影响因子：
--
通讯作者：
Shen L
中科院分区：
文献类型：
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作者：
Chung MK;Xie L;Huang SG;Wang Y;Yan J;Shen L
排列测试是在脑网络研究中用于确定统计意义的常用测试程序。不幸的是，为包含数百个对象的大规模脑成像数据集(如HCP和ADNI)生成所有可能的排列是不切实际的。以前许多加速置换测试的尝试依赖于各种近似策略，例如利用已知的参数分布估计尾部分布。在这项研究中，我们提出了一种新的换位测试，它利用了置换群的基本代数结构。该方法适用于大量扩散张量图像的脑网络差异区域定位。
The permutation test is an often used test procedure for determining statistical significance in brain network studies. Unfortunately, generating every possible permutation for large-scale brain imaging datasets such as HCP and ADNI with hundreds of subjects is not practical. Many previous attempts at speeding up the permutation test rely on various approximation strategies such as estimating the tail distribution with known parametric distributions. In this study, we propose the novel transposition test that exploits the underlying algebraic structure of the permutation group. The method is applied to a large number of diffusion tensor images in localizing the regions of the brain network differences.