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Regression Models for Correlated Biliary Data with Random Effects Assuming a Mixture of Normal Distributions

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  • Jorge Alberto Achcar

    (University of São Paulo)

  • Vanderly Janeiro

    (State University of Maringa)

  • Josmar Mazucheli

    (State University of Maringa)

Abstract

Summary Binary responses are correlated when the sampling units are clustered or when repeated binary responses are taken on the same experiment unit. In this paper we present a Bayesian analysis of logistic regression models for correlated binary data with random effects. We assume that the random effects, namely αi, i = 1, …, n are draw from a mixture of normal distributions. This assumption gives a great flexibility of fit by correlated binary data. Considering Gibbs sampling with Metropolis-Hastings algorithms, we obtain Monte Carlo estimates for the posterior quantities of interest

Suggested Citation

  • Jorge Alberto Achcar & Vanderly Janeiro & Josmar Mazucheli, 2003. "Regression Models for Correlated Biliary Data with Random Effects Assuming a Mixture of Normal Distributions," Computational Statistics, Springer, vol. 18(1), pages 39-55, March.
    • Handle: RePEc:spr:compst:v:18:y:2003:i:1:d:10.1007_s001800300131
    DOI: 10.1007/s001800300131
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    References listed on IDEAS

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    1. W. R. Gilks & N. G. Best & K. K. C. Tan, 1995. "Adaptive Rejection Metropolis Sampling Within Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(4), pages 455-472, December.
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