Abstract
Bayesian Semi-Parametric Logistic Regression Model with Application to Credit Scoring Data
Highlights
Semi-parametric regression models include regression models that combine parametric and nonparametric components
The aim of this paper is to propose a new method for estimating semi-parametric logistic regression model
It is found that the five categorical variables X5, X4, X10, X14 and X13 are the only significant variables, which influence the credit worthiness, according to our data.Three metric variables, age of the borrower, amount, and duration of the loan, are insignificant in all models
Summary
Semi-parametric regression models include regression models that combine parametric and nonparametric components. The aim of this paper is to propose a new method for estimating semi-parametric logistic regression model This is Bayesian type algorithm of estimation. Where G(.) is a known link function It is a semi-parametric model since it contains both parametric and nonparametric components. The estimation method that will be considered are based on kernel smoothing methods in the estimation of the nonparametric component of the model This method is known as the profile-likelihood method. The profile likelihood method has been introduced by Severini and Wong (1992) It is based on assuming a parametric model for the conditional distribution of Y given X and W. 4. The profile likelihood function is used to obtain an estimator of the parametric component of the model using a maximum likelihood method. The algorithm for profile likelihood method is derived as follows
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