Consider a star network where each local node possesses a set of distribution-free test statistics that exhibit a symmetric distribution around zero when their corresponding null hypothesis is true. This paper investigates statistical inference problems in networks concerning the aggregation of this general type of statistics and global error rate control under communication constraints in various scenarios. The study proposes communication-efficient algorithms that are built on established non-parametric methods, such as the Wilcoxon and sign tests, as well as modern inference methods such as the Benjamini-Hochberg (BH) and Barber-Candes (BC) procedures, coupled with sampling and quantization operations. The proposed methods are evaluated through extensive simulation studies.

翻译：考虑一个星型网络，其中每个本地节点拥有一组无分布检验统计量，当相应的原假设为真时，这些统计量呈现关于零的对称分布。本文研究了网络中关于此类通用统计量聚合的统计推断问题，以及在各种通信约束场景下的全局错误率控制。研究提出了基于已建立的非参数方法（如Wilcoxon和符号检验）以及现代推断方法（如Benjamini-Hochberg (BH) 和 Barber-Candes (BC) 过程）并耦合采样与量化操作的通信高效算法。通过广泛的模拟研究对所提出的方法进行了评估。