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A Unifying Framework for Marginalised Random-Intercept Models of Correlated Binary Outcomes

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  • Bruce J. Swihart
  • Brian S. Caffo
  • Ciprian M. Crainiceanu

Abstract

xml:lang="fr" type="main"> Nous démontrons que de nombreuses approches actuelles pour la modélisation marginale d'observations binaires conduisent à des vraisemblances correspondant à des modèles de copules. Ces copules fournissent des modèles pour l'estimation d'effets fixes marginaux et l'interprétation de structures de corrélation échangeables. En outre, nous proposons une nomenclature qui éclaire considérablement le domaine complexe des modèles à coefficients à l'origine aléatoires pour données binaires. Une collection variée d'exemples mathématiques et numériques viennent illustrer ces concepts.

Suggested Citation

  • Bruce J. Swihart & Brian S. Caffo & Ciprian M. Crainiceanu, 2014. "A Unifying Framework for Marginalised Random-Intercept Models of Correlated Binary Outcomes," International Statistical Review, International Statistical Institute, vol. 82(2), pages 275-295, August.
    • Handle: RePEc:bla:istatr:v:82:y:2014:i:2:p:275-295
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    References listed on IDEAS

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    1. Iraj Kazemi & Fatemeh Hassanzadeh, 2021. "Marginalized random-effects models for clustered binomial data through innovative link functions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 197-228, June.

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