Abstract
Credit scoring methods aim to assess credit worthiness of potential borrowers to keep the risk of credit loss low and to minimize the costs of failure over risk groups. Typical methods which are used for the statistical classification of credit applicants are linear or quadratic discriminant analysis and logistic discriminant analysis. These methods are based on scores which depend on the explanatory variables in a predefined form (usually linear). Recent methods that allow a more flexible modeling are neural networks and classification trees (see e.g. Arminger, Enache and Bonne, 1997) as well as nonparametric approaches (see e.g. Henley and Hand, 1996).
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Müller, M., Rönz, B. (2000). Credit Scoring using Semiparametric Methods. In: Franke, J., Stahl, G., Härdle, W. (eds) Measuring Risk in Complex Stochastic Systems. Lecture Notes in Statistics, vol 147. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1214-0_5
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DOI: https://doi.org/10.1007/978-1-4612-1214-0_5
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