Journal of Data Science 15(2017), 25-40 Bayesian Semi-Parametric Logistic Regression Model with Application to Credit Scoring Data Haitham M. Yousof 1 , Ahmed M. Gad 2 1 Department of Statistics, Mathematics and Insurance, Benha University, Egypt 2 Statistics Department, Faculty of Economics and Political Science, Cairo University, Egypt Abstract: In this article a new Bayesian regression model, called the Bayesian semi-parametric logistic regression model, is introduced. This model generalizes the semi-parametric logistic regression model (SLoRM) and improves its estimation process. The paper considers Bayesian and non-Bayesian estimation and inference for the parametric and semi-parametric logistic regression model with application to credit scoring data under the square error loss function. The paper introduces a new algorithm for estimating the SLoRM parameters using Bayesian theorem in more detail. Finally, the parametric logistic regression model (PLoRM), the SLoRM and the Bayesian SLoRM are used and compared using a real data set. Key words: Generalized partial linear model, semi-parametric logistic regression model, parametric logistic regression model, Profile likelihood method, Bayesian estimation, Square error loss function. 1. Introduction Semi-parametric regression models include regression models that combine parametric and nonparametric components. Semi-parametric regression models are often used in situations where the fully nonparametric model may not perform well, or when the functional form of a subset of the regressors or the density of the errors is not known. Semi-parametric regression models are a particular type of semi-parametric models containing a parametric component. So, they rely on parametric assumptions and may be misspecified and inconsistent as in a fully parametric model. Semi-parametric models combine the flexibility of a nonparametric model with the advantages of a parametric model. A fully nonparametric model will be more robust than semi- parametric and parametric models since it does not suffer from the risk of misspecification. However, nonparametric estimators suffer from low convergence rates, which deteriorate when considering higher order derivatives and multidimensional random variables. In contrast, the parametric models have the risk of misspecification, but if correctly specified they will normally enjoy √ -consistency with no deterioration caused by derivatives and multivariate data. The basic idea of a semi-parametric model is to take the best of both models.