论文标题
依赖性下的错误发现率控制的有条件校准
Conditional calibration for false discovery rate control under dependence
论文作者
论文摘要
我们在多个测试问题中引入了有限样本的错误发现率（FDR）控制的新方法，这些问题具有依赖性测试统计信息，其中依赖性是完全或部分已知的。我们的方法分别校准了每个假设的数据依赖性P值排斥阈值，从而放松或拧紧阈值以适合于靶向精确的FDR控制。除了我们的一般框架外，我们还提出了一种混凝土算法，依赖性调整后的本杰米尼·霍赫伯格（DBH）程序可以适应每个假设的Q值。在积极的回归依赖性下，DBH程序统一地主导了标准BH程序，通常它统一地主导了Benjamini-Yekutieli（通过）程序（也称为具有对数校正的BH）。模拟和真实数据示例说明了对依赖依赖的FDR控制的竞争方法的能力提高。
We introduce a new class of methods for finite-sample false discovery rate (FDR) control in multiple testing problems with dependent test statistics where the dependence is fully or partially known. Our approach separately calibrates a data-dependent p-value rejection threshold for each hypothesis, relaxing or tightening the threshold as appropriate to target exact FDR control. In addition to our general framework we propose a concrete algorithm, the dependence-adjusted Benjamini-Hochberg (dBH) procedure, which adaptively thresholds the q-value for each hypothesis. Under positive regression dependence the dBH procedure uniformly dominates the standard BH procedure, and in general it uniformly dominates the Benjamini-Yekutieli (BY) procedure (also known as BH with log correction). Simulations and real data examples illustrate power gains over competing approaches to FDR control under dependence.
