Network Regression and Supervised Centrality Estimation

• 成果类型: Article
• 署名作者: Cai, Junhui; Yang, Dan; Chen, Ran; Shen, Haipeng; Zhao, Linda; Zhu, Wu
• 署名单位: University of Notre Dame; University of Hong Kong; Washington University (WUSTL); University of Pennsylvania; Tsinghua University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2632866
• 发表日期: 2026-04-03
• 页码: 1269-1283
• 关键词: Authority centrality; Currency risk premium; global trade network; Hub centrality; network effect; community detection; social networks; identification; models; Consistency; Robustness; prediction; inference; matrix

摘要

The centrality in a network is often used to measure nodes' importance and model network effects on a certain outcome. Empirical studies widely adopt a two-stage procedure, which first estimates the centrality from the observed noisy network and then infers the network effect from the estimated centrality, even though it lacks theoretical understanding. We propose a unified modeling framework to study the properties of centrality estimation and inference and the subsequent network regression analysis with noisy network observations. Furthermore, we propose a supervised centrality estimation methodology, which aims to simultaneously estimate both centrality and network effect. We showcase the advantages of our method compared with the two-stage method both theoretically and numerically via extensive simulations and a case study in predicting currency risk premiums from the global trade network. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.