IDEAS home Printed from https://ideas.repec.org/a/oup/ajagec/v79y1997i4p1352-1362.html
   My bibliography  Save this article

Development of Statistical Discriminant Mathematical Programming Model Via Resampling Estimation Techniques

Author

Listed:
  • Houshmand A. Ziari
  • David J. Leatham
  • Paul N. Ellinger

Abstract

This paper uses resampling estimation techniques to develop a statistical mathematical programming model for discriminant analysis problems. Deleted-d jackknife, deleted-d bootstrap, and bootstrap procedures are used to identify statistical significant parameter estimates for a discriminant mathematical programming (MP) model. The results of this paper indicate that the resampling approach is a viable model selection technique. Furthermore, estimating the MP models via resampling techniques can also improve the classification performance compared to a deterministic discriminant MP model. In this study, the deleted-d jackknife procedure was the most promising among the resampling estimation techniques examined. Copyright 1997, Oxford University Press.

Suggested Citation

  • Houshmand A. Ziari & David J. Leatham & Paul N. Ellinger, 1997. "Development of Statistical Discriminant Mathematical Programming Model Via Resampling Estimation Techniques," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(4), pages 1352-1362.
    • Handle: RePEc:oup:ajagec:v:79:y:1997:i:4:p:1352-1362
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.2307/1244291
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Michael O. Olusola & Sydney I. Onyeagu, 2020. "On the binary classification problem in discriminant analysis using linear programming methods," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 30(1), pages 119-130.
    2. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    3. Khurshid Kiani, 2005. "Detecting Business Cycle Asymmetries Using Artificial Neural Networks and Time Series Models," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 65-89, August.
    4. Arriaza, M. & Gomez-Limon, J. A., 2003. "Comparative performance of selected mathematical programming models," Agricultural Systems, Elsevier, vol. 77(2), pages 155-171, August.
    5. Bildirici, Melike & Alp, Aykaç, 2008. "The Relationship Between Wages and Productivity: TAR Unit Root and TAR Cointegration Approach," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 5(1), pages 93-110.
    6. Estanislao Arana & Pedro Delicado & Luis Martí, 1999. "Validation procedures in radiological diagnostic models. Neural network and logistic regression," Economics Working Papers 414, Department of Economics and Business, Universitat Pompeu Fabra.

    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:oup:ajagec:v:79:y:1997:i:4:p:1352-1362. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.html .

    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.