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

摘要

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 α_i, 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.