正相关数据的两步多重比较方法及其在人脑网络拓扑差异检测中的应用
Two step multiple comparisons procedures for positively dependent data with application to detecting differences in human brain network topologies
摘要：
我们考虑的问题，检验正相关的多个假设假设的依赖结构的先验信息是可用的。我们提出了两步多重比较程序，利用依赖结构的先验信息，而不依赖于强有力的假设。在第一步中，我们将测试分组为子集，其中测试应该是正相关的，并且在每个子集中，我们计算测试分数的标准化平均值。给定子集平均得分或等效的子集p值，我们在预定义的阈值处应用第一筛选，这导致两种类型的子集。基于这种类型，修改了原始单次检验p值，以便它们可以与任何多重比较程序结合使用。我们通过不同的仿真表明，功率获得与建议的两步方法，并比较它与传统的多重比较程序。作为一个例子，我们的方法应用于实际数据比较两组人脑网络之间的拓扑差异。
英文摘要：
We consider the problem of testing positively dependent multiple hypotheses assuming that a prior information about the dependence structure is available. We propose two-step multiple comparisons procedures that exploit the prior information of the dependence structure, without relying on strong assumptions. In the first step, we group the tests into subsets where tests are supposed to be positively dependent and in each of which we compute the standardized mean of the test scores. Given the subset mean scores or equivalently the subsets p-values, we apply a first screening at a predefined threshold, which results in two types of subsets. Based on this typing, the original single test p-values are modified such that they can be used in conjunction with any multiple comparison procedure. We show by means of different simulation that power is gained with the proposed two-step methods, and compare it with traditional multiple comparison procedures. As an illustration, our method is applied on real data comparing topological differences between two groups of human brain networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
