CREDIT SCORING MODELING WITH STATE-DEPENDENT SAMPLE SELECTION: A COMPARISON STUDY WITH THE USUAL LOGISTIC MODELING

Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model, a logistic regression with state-dependent sample selectio...