论文标题
样本和前向：网络中错误发现率的沟通有效控制
Sample-and-Forward: Communication-Efficient Control of the False Discovery Rate in Networks
论文作者
论文摘要
这项工作涉及在通信约束下控制网络中的错误发现率（FDR）。我们提出了带有一般拓扑的多主网络的Benjamini-Hochberg（BH）程序的样本和前方，这是一个灵活且沟通的版本。我们的方法证明了网络中的节点不需要彼此传达p值以在全球FDR控制约束下实现不错的统计能力。考虑一个总共具有$ M $ p值的网络，我们的方法包括首先在每个节点处对P值的（经验）CDF进行采样，然后将$ \ Mathcal {o}（\ log M）$位转发给其邻居。在与原始BH程序相同的假设下，我们的方法既具有可证明的有限样本FDR控制，也具有竞争性的经验检测能力，即使每个节点有几个样本。我们在p值的混合模型假设下提供了对功率的渐近分析。
This work concerns controlling the false discovery rate (FDR) in networks under communication constraints. We present sample-and-forward, a flexible and communication-efficient version of the Benjamini-Hochberg (BH) procedure for multihop networks with general topologies. Our method evidences that the nodes in a network do not need to communicate p-values to each other to achieve a decent statistical power under the global FDR control constraint. Consider a network with a total of $m$ p-values, our method consists of first sampling the (empirical) CDF of the p-values at each node and then forwarding $\mathcal{O}(\log m)$ bits to its neighbors. Under the same assumptions as for the original BH procedure, our method has both the provable finite-sample FDR control as well as competitive empirical detection power, even with a few samples at each node. We provide an asymptotic analysis of power under a mixture model assumption on the p-values.
