1 research outputs found
Communication-Efficient Distribution-Free Inference Over Networks
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.Comment: Accepted to the Asilomar Conference on Signals, Systems, and
Computers (2023