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，其中每个局部节点拥有一组在相应原假设成立时围绕零对称分布的无分布检验统计量。本文研究网络中的统计推断问题，涉及此类通用型统计量的聚合以及在不同场景下通信约束条件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检验和符号检验）以及现代推断方法（如Benjamini-Hochberg（BH）和Barber-Candes（BC）过程）的通信高效算法，并结合了采样和量化操作。所提方法通过大量模拟研究进行了评估。