The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Generalized Partially Linear Additive Models for Credit Scoring

  • Shim, Ju-Hyun (Department of Statistics, Seoul National University) ;
  • Lee, Young-K. (Department of Statistics, Kangwon National University)
  • Received : 20110500
  • Accepted : 20110700
  • Published : 2011.08.31
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Abstract

Credit scoring is an objective and automatic system to assess the credit risk of each customer. The logistic regression model is one of the popular methods of credit scoring to predict the default probability; however, it may not detect possible nonlinear features of predictors despite the advantages of interpretability and low computation cost. In this paper, we propose to use a generalized partially linear model as an alternative to logistic regression. We also introduce modern ensemble technologies such as bagging, boosting and random forests. We compare these methods via a simulation study and illustrate them through a German credit dataset.

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References

  1. Breiman, L. (1994). Heuristics of Instability in Model Selection, Technical Report, Statistics Department, University of California at Berkeley.
  2. Breiman, L. (1996). Bagging predictors, Machine Learning, 24, 123-140.
  3. Breiman, L. (2001). Random forests, Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
  4. Buja, A., Hastie, T. and Tibshirani, R. (1989). Linear smoothers and additive models (with discussion), Annals of Statistics, 17, 453-555. https://doi.org/10.1214/aos/1176347115
  5. Friedman, J., Hastie, T. and Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting, Annals of Statistics, 28, 337-374.
  6. Friedman, J., Hastie, T. and Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting, Annals of Statistics, 28, 337-374.
  7. Mammen, E., Linton, O. and Nielsen, J. P. (1999). The existence and asymptotic properties of a backfitting projection algorithm under weak conditions, Annals of Statistics, 27, 1443-1490.
  8. Mays, E. (2001). Handbook of Credit Scoring, Fitzroy Dearborn Pub, London.
  9. Severini, T. A. and Staniswalis, J. G. (1994). Quasi-Likelihood estimation in semiparametric models, Journal of American Statistical Association, 89, 501-511. https://doi.org/10.2307/2290852
  10. Thomas, L. C., Edelman, D. B. and Crook, J. N. (2002). Credit Scoring and Its Applications, SIAM Society of Industrial and Applied Mathematics, Philadelphia.
  11. Wang, L., Liu, X., Liang, H. and Carroll, R. (2011). Generalized additive partial linear models - polynomial spline smoothing estimation and variable selection procedures, Annals of Statistics, in print.
  12. Yu, K. and Lee, Y. K. (2010). Efficient semiparametric estimation in generalized partially linear additive models, Journal of Korean Statistical Society, 39, 299-304. https://doi.org/10.1016/j.jkss.2010.02.001
  13. Yu, K., Park, B. U. and Mammen, E. (2008). Smooth backfitting in generalized additive models, Annals of Statistics, 36, 228-260. https://doi.org/10.1214/009053607000000596