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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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    File URL: http://hdl.handle.net/10.1111/insr.12035
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    References listed on IDEAS

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    1. Klaus Larsen & Jørgen Holm Petersen & Esben Budtz-Jørgensen & Lars Endahl, 2000. "Interpreting Parameters in the Logistic Regression Model with Random Effects," Biometrics, The International Biometric Society, vol. 56(3), pages 909-914, September.
    2. Daniel Berg, 2009. "Copula goodness-of-fit testing: an overview and power comparison," The European Journal of Finance, Taylor & Francis Journals, vol. 15(7-8), pages 675-701.
    3. Beunckens, Caroline & Sotto, Cristina & Molenberghs, Geert, 2008. "A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1533-1548, January.
    4. Yan, Jun, 2007. "Enjoy the Joy of Copulas: With a Package copula," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i04).
    5. Chandra Bhat & Ipek Sener, 2009. "A copula-based closed-form binary logit choice model for accommodating spatial correlation across observational units," Journal of Geographical Systems, Springer, vol. 11(3), pages 243-272, September.
    6. Caffo, Brian & An, Ming-Wen & Rohde, Charles, 2007. "Flexible random intercept models for binary outcomes using mixtures of normals," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5220-5235, July.
    7. N. Rao Chaganty & Harry Joe, 2004. "Efficiency of generalized estimating equations for binary responses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 851-860, November.
    8. Joe, Harry, 2005. "Asymptotic efficiency of the two-stage estimation method for copula-based models," Journal of Multivariate Analysis, Elsevier, vol. 94(2), pages 401-419, June.
    9. Patrick J. Heagerty, 1999. "Marginally Specified Logistic-Normal Models for Longitudinal Binary Data," Biometrics, The International Biometric Society, vol. 55(3), pages 688-698, September.
    10. Zengri Wang, 2003. "Matching conditional and marginal shapes in binary random intercept models using a bridge distribution function," Biometrika, Biometrika Trust, vol. 90(4), pages 765-775, December.
    11. Agresti, Alan & Caffo, Brian & Ohman-Strickland, Pamela, 2004. "Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 639-653, October.
    12. Nikoloulopoulos, Aristidis K. & Karlis, Dimitris, 2008. "Copula model evaluation based on parametric bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3342-3353, March.
    13. Lanjia Lin & Dipankar Bandyopadhyay & Stuart R. Lipsitz & Debajyoti Sinha, 2010. "Association Models for Clustered Data with Binary and Continuous Responses," Biometrics, The International Biometric Society, vol. 66(1), pages 287-293, March.
    14. N. Rao Chaganty & Harry Joe, 2006. "Range of correlation matrices for dependent Bernoulli random variables," Biometrika, Biometrika Trust, vol. 93(1), pages 197-206, March.
    15. Xiaoyun Li & Dipankar Bandyopadhyay & Stuart Lipsitz & Debajyoti Sinha, 2011. "Likelihood Methods for Binary Responses of Present Components in a Cluster," Biometrics, The International Biometric Society, vol. 67(2), pages 629-635, June.
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    Cited by:

    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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