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Mining the customer credit using classification and regression tree and multivariate adaptive regression splines

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  • Lee, Tian-Shyug
  • Chiu, Chih-Chou
  • Chou, Yu-Chao
  • Lu, Chi-Jie
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 50 (2006)
    Issue (Month): 4 (February)
    Pages: 1113-1130

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    Handle: RePEc:eee:csdana:v:50:y:2006:i:4:p:1113-1130

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    1. Wiginton, John C., 1980. "A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(03), pages 757-770, September.
    2. Jae-Chan Kim & Dae-Ho Kim & Jae-Jun Kim & Jong-Suk Ye & Hyun-Soo Lee, 2000. "Segmenting the Korean housing market using multiple discriminant analysis," Construction Management and Economics, Taylor & Francis Journals, vol. 18(1), pages 45-54.
    3. Jagielska, Ilona & Jaworski, Janusz, 1996. "Neural Network for Predicting the Performance of Credit Card Accounts," Computational Economics, Society for Computational Economics, vol. 9(1), pages 77-82, February.
    4. De Gooijer, Jan G. & Ray, Bonnie K. & Krager, Horst, 1998. "Forecasting exchange rates using TSMARS," Journal of International Money and Finance, Elsevier, vol. 17(3), pages 513-534, June.
    5. Bardos, Mireille, 1998. "Detecting the risk of company failure at the Banque de France," Journal of Banking & Finance, Elsevier, vol. 22(10-11), pages 1405-1419, October.
    6. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, 09.
    7. Piramuthu, Selwyn, 1999. "Financial credit-risk evaluation with neural and neurofuzzy systems," European Journal of Operational Research, Elsevier, vol. 112(2), pages 310-321, January.
    8. Trevino, Len J. & Daniels, John D., 1995. "FDI theory and foreign direct investment in the United States: a comparison of investors and non-investors," International Business Review, Elsevier, vol. 4(2), pages 177-194, June.
    9. Kuhnert, Petra M. & Do, Kim-Anh & McClure, Rod, 2000. "Combining non-parametric models with logistic regression: an application to motor vehicle injury data," Computational Statistics & Data Analysis, Elsevier, vol. 34(3), pages 371-386, September.
    10. Westgaard, Sjur & van der Wijst, Nico, 2001. "Default probabilities in a corporate bank portfolio: A logistic model approach," European Journal of Operational Research, Elsevier, vol. 135(2), pages 338-349, December.
    11. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    12. Laitinen, Erkki K., 1999. "Predicting a corporate credit analyst's risk estimate by logistic and linear models," International Review of Financial Analysis, Elsevier, vol. 8(2), pages 97-121, June.
    13. Barney, Douglas K. & Finley Graves, O. & Johnson, John D., 1999. "The farmers home administration and farm debt failure prediction," Journal of Accounting and Public Policy, Elsevier, vol. 18(2), pages 99-139.
    14. Desai, Vijay S. & Crook, Jonathan N. & Overstreet, George A., 1996. "A comparison of neural networks and linear scoring models in the credit union environment," European Journal of Operational Research, Elsevier, vol. 95(1), pages 24-37, November.
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    Cited by:
    1. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
    2. Shiyi Chen & Kiho Jeong & Wolfgang K. Härdle, 2008. "Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns," SFB 649 Discussion Papers SFB649DP2008-051, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
    4. Mostafa, Mohamed M. & Nataraajan, Rajan, 2009. "A neuro-computational intelligence analysis of the ecological footprint of nations," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3516-3531, July.
    5. Evžen Kocenda & Martin Vojtek, 2009. "Default Predictors and Credit Scoring Models for Retail Banking," CESifo Working Paper Series 2862, CESifo Group Munich.
    6. Reynes, Christelle & Sabatier, Robert & Molinari, Nicolas, 2006. "Choice of B-splines with free parameters in the flexible discriminant analysis context," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1765-1778, December.
    7. Ayşe Özmen & Gerhard-Wilhelm Weber & Zehra Çavuşoğlu & Özlem Defterli, 2013. "The new robust conic GPLM method with an application to finance: prediction of credit default," Journal of Global Optimization, Springer, vol. 56(2), pages 233-249, June.

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