论文标题
大规模推理的沟通效率分布式多重测试
Communication-Efficient Distributed Multiple Testing for Large-Scale Inference
论文作者
论文摘要
Benjamini-Hochberg（BH）程序是一种具有虚假发现率（FDR）控制的多次测试的著名方法。在本文中，我们考虑了每个节点都具有大量p值的大规模分布式网络，目标是以通信有效的方式实现全局BH性能。我们建议每个节点都根据（估计）的真实零假设（估计）全局比例进行调整后的测试尺寸进行局部测试。有了合适的假设，我们的方法在渐近上等同于全局BH程序。在此激励的情况下，我们开发了一种针对星网络的算法，每个节点只需要传输（局部）nulls和（局部）p值的（局部）p值的算法；然后，中心节点将一个参数（基于全局估计和测试大小计算）到本地节点。在“实验”部分中，我们利用现有的真实零值比例的估计值，并考虑各种设置来评估我们方法的性能和鲁棒性。
The Benjamini-Hochberg (BH) procedure is a celebrated method for multiple testing with false discovery rate (FDR) control. In this paper, we consider large-scale distributed networks where each node possesses a large number of p-values and the goal is to achieve the global BH performance in a communication-efficient manner. We propose that every node performs a local test with an adjusted test size according to the (estimated) global proportion of true null hypotheses. With suitable assumptions, our method is asymptotically equivalent to the global BH procedure. Motivated by this, we develop an algorithm for star networks where each node only needs to transmit an estimate of the (local) proportion of nulls and the (local) number of p-values to the center node; the center node then broadcasts a parameter (computed based on the global estimate and test size) to the local nodes. In the experiment section, we utilize existing estimators of the proportion of true nulls and consider various settings to evaluate the performance and robustness of our method.
