A Regression Framework for Studying Relationships among Attributes under Network Interference

• 成果类型: Article
• 署名作者: Fritz, Cornelius; Schweinberger, Michael; Bhadra, Subhankar; Hunter, David R.
• 署名单位: Trinity College Dublin; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2565851
• 发表日期: 2026-04-03
• 页码: 1349-1360
• 关键词: dependent data; generalized linear models; Minorization-Maximization; pseudo-likelihood; model selection

摘要

To understand how the interconnected and interdependent world of the twenty-first century operates and make model-based predictions, joint probability models for networks and interdependent outcomes are needed. We propose a comprehensive regression framework for networks and interdependent outcomes with multiple advantages, including interpretability, scalability, and provable theoretical guarantees. The regression framework can be used for studying relationships among attributes of connected units and captures complex dependencies among connections and attributes, while retaining the virtues of linear regression, logistic regression, and other regression models by being interpretable and widely applicable. On the computational side, we show that the regression framework is amenable to scalable statistical computing based on convex optimization of pseudo-likelihoods using minorization-maximization methods. On the theoretical side, we establish convergence rates for pseudo-likelihood estimators based on a single observation of dependent connections and attributes. We demonstrate the regression framework using simulations and an application to hate speech on the social media platform X. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.