Variational approximations in Bayesian model selection for finite mixture distributions
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Estimation in the probit normal model for binary outcomes using the SAEM algorithm
Computational Statistics & Data Analysis
Bayesian projection approaches to variable selection in generalized linear models
Computational Statistics & Data Analysis
State space mixed models for binary responses with scale mixture of normal distributions links
Computational Statistics & Data Analysis
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A flexible Bayesian approach to a generalized linear model is proposed to describe the dependence of binary data on explanatory variables. The inverse of the exponential power cumulative distribution function is used as the link to the binary regression model. The exponential power family provides distributions with both lighter and heavier tails compared to the normal distribution, and includes the normal and an approximation to the logistic distribution as particular cases. The idea of using a data augmentation framework and a mixture representation of the exponential power distribution is exploited to derive efficient Gibbs sampling algorithms for both informative and noninformative settings. Some examples are given to illustrate the performance of the proposed approach when compared with other competing models.