A0288
Title: Nonparametric inference on network effects of general relationship network data
Authors: Wen Zhou - New York University (United States) [presenting]
Yuan Zhang - The Ohio State University (United States)
Wenqin Du - Colorado State University (United States)
Abstract: In recent years, the relationship network has received significant attention for its ability to provide unique insights into agent interactions across various fields. Most existing studies have primarily focused on modelling the association between the relationship network and other covariates using arguably restrictive parametric models while largely overlooking the inference of network effects, such as the reciprocal or sender-receiver effect. Testing network effects within a relationship network are particularly challenging due to edge dependence, which renders permutation-based methods inapplicable. The testing statistics utilize the reduced U-statistics and admit analytically tractable limiting distributions, overcoming the nontrivial sampling distributions of network moment statistics on relationship network effects caused by degeneration and indeterminacy of degeneracy order. The theoretical guarantee of the testing framework is established by investigating the Berry-Esseen bounds for the testing statistics. To showcase the practicality of the methods, two real-world relationship networks are applied to them, one in international trade and the other in faculty hiring networks.
