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Cited by 346

The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.

Bilinear Mixed-Effects Models for Dyadic Data

Hoff, Peter D·2005·Journal of the American Statistical Association

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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Metadata

Authors
Hoff, P. D.
Venue
Journal of the American Statistical Association
Published
2005-03
Volume
100
Issue
469
Pages
286-295
ISSN
  • 0162-1459 (ISSN-L)
  • 1537-274X
OpenAlex
W2123082179
Cited by
346
References
37
FWCI
10.30
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Bilinear Mixed-Effects Models for Dyadic Data. https://app.cochat.ai/papers/10.1198/016214504000001015
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Hoff, P. D. (2005). Bilinear Mixed-Effects Models for Dyadic Data. Journal of the American Statistical Association. https://doi.org/10.1198/016214504000001015

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.