Development of statistical discriminant mathematical programming model via resampling estimation techniques Academic Article

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.

authors

• Leatham, David

published proceedings

• AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS

author list (cited authors)

• Ziari, H. A., Leatham, D. J., & Ellinger, P. N.

citation count

• 11

complete list of authors

• Ziari, HA||Leatham, DJ||Ellinger, PN

publication date

• January 1997

publisher

• Wiley  Publisher

published in

• American Journal of Agricultural Economics  Journal

keywords

• Credit Scoring
• Logit
• Model Selection
• Resampling
• Statistical Mathematical Program

Digital Object Identifier (DOI)

• 10.2307/1244291

start page

• 1352

end page

• 1362

volume

• 79

issue

• 4

URL

• http%3A%2F%2Fdx.doi.org%2F10.2307%2F1244291