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Development of Statistical Discriminant Mathematical Programming Model Via Resampling Estimation Techniques

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  • 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
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    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.

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