Bilinear mixed-effects models for dyadic data

• 成果类型: Article
• 署名作者: Hoff, PD
• 署名单位: University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000001015
• 发表日期: 2005
• 页码: 286-295
• 关键词: GENERALIZED LINEAR-MODELS; round-robin analysis; social networks; dedicom model; inference; variance; prediction

摘要

This article discusses the use of asymmetric multiplicative interaction effect to capture certain types of third-order dependence patterns often present in social networks and other dyadic datasets. Such an effect, along with standard linear fixed and random effects, is incorporated into a generalized linear model, and a Markov chain Monte Carlo algorithm is provided for Bayesian estimation and inference. In an example analysis of international relations data, accounting for such patterns improves model fit and predictive performance.