IDEAS home Printed from https://ideas.repec.org/a/eee/jbfina/v28y2004i4p857-874.html
   My bibliography  Save this article

Does reject inference really improve the performance of application scoring models?

Author

Listed:
  • Crook, Jonathan
  • Banasik, John

Abstract

No abstract is available for this item.

Suggested Citation

  • Crook, Jonathan & Banasik, John, 2004. "Does reject inference really improve the performance of application scoring models?," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 857-874, April.
    • Handle: RePEc:eee:jbfina:v:28:y:2004:i:4:p:857-874
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378-4266(03)00203-6
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Reichert, Alan K & Cho, Chien-Ching & Wagner, George M, 1983. "An Examination of the Conceptual Issues Involved in Developing Credit-scoring Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 101-114, April.
    2. James J. Heckman, 2001. "Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture," Journal of Political Economy, University of Chicago Press, vol. 109(4), pages 673-748, August.
    3. J Banasik & J Crook & L Thomas, 2003. "Sample selection bias in credit scoring models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 822-832, August.
    4. Boyes, William J. & Hoffman, Dennis L. & Low, Stuart A., 1989. "An econometric analysis of the bank credit scoring problem," Journal of Econometrics, Elsevier, vol. 40(1), pages 3-14, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ha-Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," EconomiX Working Papers 2016-10, University of Paris Nanterre, EconomiX.
    2. Ha Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," Working Papers hal-04141601, HAL.
    3. Y Kim & S Y Sohn, 2007. "Technology scoring model considering rejected applicants and effect of reject inference," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(10), pages 1341-1347, October.
    4. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    5. Rogelio A. Mancisidor & Michael Kampffmeyer & Kjersti Aas & Robert Jenssen, 2019. "Deep Generative Models for Reject Inference in Credit Scoring," Papers 1904.11376, arXiv.org, revised Sep 2021.
    6. Bourguignon, Francois, 2005. "The Effect of Economic Growth on Social Structures," Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 1, chapter 27, pages 1701-1747, Elsevier.
    7. Marco Costanigro & Yuko Onozaka, 2020. "A Belief‐Preference Model of Choice for Experience and Credence Goods," Journal of Agricultural Economics, Wiley Blackwell, vol. 71(1), pages 70-95, February.
    8. Paolo Giovanni Piacquadio, 2017. "A Fairness Justification of Utilitarianism," Econometrica, Econometric Society, vol. 85, pages 1261-1276, July.
    9. Gabriel, Stuart A. & Rosenthal, Stuart S., 2005. "Homeownership in the 1980s and 1990s: aggregate trends and racial gaps," Journal of Urban Economics, Elsevier, vol. 57(1), pages 101-127, January.
    10. A.J. Feelders, 2000. "Credit scoring and reject inference with mixture models," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(1), pages 1-8, March.
    11. Pennings, Joost M.E. & Garcia, Philip & Irwin, Scott H. & Good, Darrel L., 2003. "How To Group Market Participants? Heterogeneity In Hedging Behavior," 2003 Annual meeting, July 27-30, Montreal, Canada 21963, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    12. Marco Caliendo & Reinhard Hujer, 2006. "The microeconometric estimation of treatment effects—An overview," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 199-215, March.
    13. Roy Allen & John Rehbeck, 2020. "Identification of Random Coefficient Latent Utility Models," Papers 2003.00276, arXiv.org.
    14. He, Xue-Zhong & Li, Kai & Santi, Caterina & Shi, Lei, 2022. "Social interaction, volatility clustering, and momentum," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 125-149.
    15. Matloob Piracha & Massimiliano Tani & Florin Vadean, 2012. "Immigrant over- and under-education: the role of home country labour market experience," IZA Journal of Migration and Development, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 1(1), pages 1-21, December.
    16. Darren Hudson & Cary W. Herndon, 2002. "Factors influencing probability and frequency of participation in merger and partnership activity in agricultural cooperatives," Agribusiness, John Wiley & Sons, Ltd., vol. 18(2), pages 231-246.
    17. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    18. Machin Stephen & Puhani Patrick A., 2005. "Special Issue on the Economics of Education – Policies and Empirical Evidence: Editorial," German Economic Review, De Gruyter, vol. 6(3), pages 259-267, August.
    19. Greenstone, Michael & Gayer, Ted, 2009. "Quasi-experimental and experimental approaches to environmental economics," Journal of Environmental Economics and Management, Elsevier, vol. 57(1), pages 21-44, January.
    20. Christiane Hinerasky, 2014. "Advances in Training Evaluation - Psychological, Educational, Economic, and Econometric Perspectives on the Kirkpatrick Model," Working Papers Dissertations 14, Paderborn University, Faculty of Business Administration and Economics.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbfina:v:28:y:2004:i:4:p:857-874. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbf .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.