IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this article or follow this journal

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

  • Lee, Tian-Shyug
  • Chiu, Chih-Chou
  • Chou, Yu-Chao
  • Lu, Chi-Jie
Registered author(s):

    No abstract is available for this item.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL: http://www.sciencedirect.com/science/article/B6V8V-4DYVTPW-1/2/a174ca1bfbf6ab8e5c635e4ab80d24d3
    Download Restriction: Full text for ScienceDirect subscribers only.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

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

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

    as
    in new window

    Handle: RePEc:eee:csdana:v:50:y:2006:i:4:p:1113-1130
    Contact details of provider: Web page: http://www.elsevier.com/locate/csda

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    as in new window
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:50:y:2006:i:4:p:1113-1130. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.