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Random Effects Modeling of Multiple Binomial Responses Using the Multivariate Binomial Logit-Normal Distribution

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  • Brent A. Coull
  • Alan Agresti

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  • Brent A. Coull & Alan Agresti, 2000. "Random Effects Modeling of Multiple Binomial Responses Using the Multivariate Binomial Logit-Normal Distribution," Biometrics, The International Biometric Society, vol. 56(1), pages 73-80, March.
    • Handle: RePEc:bla:biomet:v:56:y:2000:i:1:p:73-80
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    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2000.00073.x
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    References listed on IDEAS

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    1. Paul Catalano & Louise Ryan & Daniel Scharfstein, 1994. "Modeling Fetal Death and Malformation in Developmental Toxicity Studies," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 629-637, August.
    2. Brent A. Coull & Alan Agresti, 1999. "The Use of Mixed Logit Models to Reflect Heterogeneity in Capture-Recapture Studies," Biometrics, The International Biometric Society, vol. 55(1), pages 294-301, March.
    3. J. G. Booth & J. P. Hobert, 1999. "Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 265-285.
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    2. Alan Agresti & Ivy Liu, 2001. "Strategies for Modeling a Categorical Variable Allowing Multiple Category Choices," Sociological Methods & Research, , vol. 29(4), pages 403-434, May.
    3. S. Rabe-Hesketh & A. Skrondal, 2001. "Parameterization of Multivariate Random Effects Models for Categorical Data," Biometrics, The International Biometric Society, vol. 57(4), pages 1256-1263, December.
    4. Sara Amoroso, 2017. "Multilevel heterogeneity of R&D cooperation and innovation determinants," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 7(1), pages 93-120, April.
    5. John B. Holmes & Matthew R. Schofield & Richard J. Barker, 2022. "Pólya‐gamma data augmentation and latent variable models for multivariate binomial data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 194-218, January.
    6. Alessio Farcomeni, 2015. "Latent class recapture models with flexible behavioural response," Statistica, Department of Statistics, University of Bologna, vol. 75(1), pages 5-17.
    7. Chen, Hsiang-Chun & Wehrly, Thomas E., 2016. "Approximate uniform shrinkage prior for a multivariate generalized linear mixed model," Journal of Multivariate Analysis, Elsevier, vol. 145(C), pages 148-161.
    8. Parsons, Nick R. & Costa, Matthew L. & Achten, Juul & Stallard, Nigel, 2009. "Repeated measures proportional odds logistic regression analysis of ordinal score data in the statistical software package R," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 632-641, January.
    9. Amoroso, S., 2013. "Heterogeneity of innovative, collaborative, and productive firm-level processes," Other publications TiSEM f5784a49-7053-401d-855d-1, Tilburg University, School of Economics and Management.
    10. Jane Osburn, 2011. "A Latent Variable Approach to Examining the Effects of HR Policies on the Inter- and Intra-Establishment Wage and Employment Structure: A Study of Two Precision Manufacturing Industries," Working Papers 451, U.S. Bureau of Labor Statistics.
    11. Lisa Bellinghausen & Nicolas Vaillant, 2010. "Les déterminants du stress professionnel ressenti : une estimation par la méthode des équations d’estimation généralisées," Économie et Prévision, Programme National Persée, vol. 195(4), pages 67-82.
    12. Gholamreza Oskrochi & Emmanuel Lesaffre & Youssof Oskrochi & Delva Shamley, 2016. "An Application of the Multivariate Linear Mixed Model to the Analysis of Shoulder Complexity in Breast Cancer Patients," IJERPH, MDPI, vol. 13(3), pages 1-13, March.
    13. Victor De Oliveira, 2017. "Geostatistical Binary Data: Models, Properties And Connections," Working Papers 0151mss, College of Business, University of Texas at San Antonio.
    14. Angelo Moretti, 2023. "Estimation of small area proportions under a bivariate logistic mixed model," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3663-3684, August.

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