Bilinear Mixed Effects Models for Dyadic Data

Abstract

This article discusses the use of a symmetric 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.

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Document Details

Document Type
Technical Report
Publication Date
Jul 02, 2003
Accession Number
ADA459832

Entities

People

  • Peter D. Hoff

Organizations

  • University of Washington

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Analysis Of Variance
  • Central Asia
  • Data Analysis
  • Data Mining
  • Data Science
  • Information Science
  • International Relations
  • Markov Chains
  • Maximum Likelihood Estimation
  • Monte Carlo Method
  • Probability
  • Sampling
  • Statistical Algorithms
  • Statistical Analysis
  • United States
  • Ussr

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Organizational Psychology.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference