在非参数图依赖模型下控制错误发现率
Controlling the false discovery rate under a non-parametric graphical dependence model
AI总结：
提出在已知依赖图的非参数模型下控制多重检验错误发现率的高效方法，其拒绝集在无依赖边时与BH程序一致，在边较少时功效接近BH，且计算高效，适用于大规模假设检验。
摘要：
我们提出了在多重检验中控制错误发现率的充分条件和计算高效的程序，条件是$p$值通过已知的依赖图相互关联——这意味着我们假设不在彼此邻域内的$p$值是独立的，但其他依赖关系未指定。当BH拒绝集之间没有边时，我们方法的拒绝集与Benjamini--Hochberg（BH）程序一致，并且在模拟和基因组学数据示例中发现，当此类边很少时（这很常见），其功效接近BH程序。由于我们的方法忽略了所有不在BH拒绝集中的假设，因此当该集合较小时，计算效率很高。我们最快的方法IndBH程序，即使在多达一百万个假设的模拟中，通常也能在几秒内完成。
英文摘要：
We propose sufficient conditions and computationally efficient procedures for false discovery rate control in multiple testing when the $p$-values are related by a known \emph{dependency graph} -- meaning that we assume independence of $p$-values that are not within each other's neighborhoods, but otherwise leave the dependence unspecified. Our methods' rejection sets coincide with that of the Benjamini--Hochberg (BH) procedure whenever there are no edges between BH rejections, and we find in simulations and a genomics data example that their power approaches that of the BH procedure when there are few such edges, as is commonly the case. Because our methods ignore all hypotheses not in the BH rejection set, they are computationally efficient whenever that set is small. Our fastest method, the IndBH procedure, typically finishes within seconds even in simulations with up to one million hypotheses.
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