Skewed link-based regression models for misclassified binary data

Abstract

In this paper, we propose flexible Bayesian approaches for binary regression models in the presence of misclassified data. These approaches consider asymmetric links based on the skew-normal and the asymmetric exponential power distributions. The computational difficulties have been avoided by using data augmentation schemes. The idea of using data augmentation schemes with two types of latent variables is exploited to derive efficient MCMC algorithms. A simulation study and an application illustrate the model performance in comparison with the standard methods that do not consider skewness and/or which do not consider misclassification.