指数随机图模型
计量经济学
推论
图形
统计推断
随机效应模型
指数函数
随机图
混淆
规范
计算机科学
统计
数学
人工智能
理论计算机科学
内科学
荟萃分析
数学分析
医学
作者
Janet M. Box-Steffensmeier,Ben Campbell,Dino P. Christenson,Jason W. Morgan
标识
DOI:10.1016/j.socnet.2019.07.002
摘要
Exponential Random Graph Models (ERGMs) are an increasingly common tool for inferential network analysis. However, a potential problem for these models is the assumption of correct model specification. Through six substantive applications (Mesa High, Florentine Marriage, Military Alliances, Militarized Interstate Disputes, Regional Planning, Brain Complexity), we illustrate how unobserved heterogeneity and confounding leads to degenerate model specifications, inferential errors, and poor model fit. In addition, we present evidence that a better approach exists in the form of the Frailty Exponential Random Graph Model (FERGM), which extends the ERGM to account for unit or group-level heterogeneity in tie formation. In each case, the ERGM is prone to producing inferential errors and forecasting ties with lower accuracy than the FERGM.
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