| publication name | Bayesian Semi-Parametric Logistic Regression Model with Application to Credit Scoring Data |
|---|---|
| Authors | Haitham M. Yousof, Ahmed M. Gad |
| year | 2016 |
| keywords | Generalized partial linear model, semi-parametric logistic regression model, parametric logistic regression model, Profile likelihood method, Bayesian estimation, Square error loss function. |
| journal | JDS |
| volume | Not Available |
| issue | Not Available |
| pages | Not Available |
| publisher | Not Available |
| Local/International | International |
| Paper Link | Not Available |
| Full paper | download |
| Supplementary materials | Not Available |
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 estimations. This 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. This 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.