Correlated binomial regression models
In this paper, a class of correlated binomial regression models is proposed. The model is based on the generalized binomial distribution proposed by Luceo (1995) and Luceo and Ceballos (1995). The regression structure is modeled by using four different link functions and the dependence between the Bernoulli trials is modeled by using three different correlation functions. A data augmentation scheme is used in order to overcome the complexity of the mixture likelihood. A Bayesian method for inference is developed for the proposed model which relies on both the data augmentation scheme and the MCMC algorithms to obtain the posterior estimate for the parameters. Two types of Bayesian residuals and a local influence measure from a Bayesian perspective are proposed to check the underlying model assumptions, as well as to identify the presence of outliers and/or influential observations. Simulation studies are presented in order to illustrate the performance of the developed methodology. A real data set is analyzed by using the proposed models.
binomial regression models generalized binomial distribution data augmentation scheme bayesian residuals local influence bayesian inference