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Famous for its easy implementation, Logistic Regression has been the primary model in credit scoring. Albeit attractively simple, it is criticized for failing to capture nonlinearity and nonmonotonicity and therefore possibly not leads to satisfactory results. Introduced by Hastie and Tibshirani, Generalized Additive Model (GAM) provides the ability to detect the nonlinear and nonmonotonic relationship between the risk behavior and predictors without the loss of interpretability. However, the semi-parametric nature of GAM makes it difficult to be used in a business. This paper presents an application of GAM in credit scoring as well as a class of hybrid models combining ideas of Logistic Regression and GAM in order to improve the generalizability of nonlinear modeling.

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Format:PDFSize:219 KB
Date:Mar 2009
Pages:14
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