Abstract
There are numerous variable selection rules in classical discriminant analysis. These rules enable a researcher to distinguish significant variables from nonsignificant ones and thus provide a parsimonious classification model based solely on significant variables. Prominent among such rules are the forward and backward stepwise variable selection criteria employed in statistical software packages such as Statistical Package for the Social Sciences and BMDP Statistical Software. No such criterion currently exists for linear programming (LP) approaches to discriminant analysis. In this paper, a criterion is developed to distinguish significant from nonsignificant variables for use in LP models. This criterion is based on the “jackknife” methodology. Examples are presented to illustrate implementation of the proposed criterion.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 554-563 |
| Number of pages | 10 |
| Journal | Decision Sciences |
| Volume | 19 |
| Issue number | 3 |
| DOIs | |
| State | Published - 1988 |
| Externally published | Yes |
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All Science Journal Classification (ASJC) codes
- Business, Management and Accounting(all)
- Strategy and Management
- Information Systems and Management
- Management of Technology and Innovation
Cite this
A Variable Selection Criterion in the Linear Programming Approaches to Discriminant Analysis. / Nath, Ravinder; Jones, Thomas W.
In: Decision Sciences, Vol. 19, No. 3, 1988, p. 554-563.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A Variable Selection Criterion in the Linear Programming Approaches to Discriminant Analysis
AU - Nath, Ravinder
AU - Jones, Thomas W.
PY - 1988
Y1 - 1988
N2 - There are numerous variable selection rules in classical discriminant analysis. These rules enable a researcher to distinguish significant variables from nonsignificant ones and thus provide a parsimonious classification model based solely on significant variables. Prominent among such rules are the forward and backward stepwise variable selection criteria employed in statistical software packages such as Statistical Package for the Social Sciences and BMDP Statistical Software. No such criterion currently exists for linear programming (LP) approaches to discriminant analysis. In this paper, a criterion is developed to distinguish significant from nonsignificant variables for use in LP models. This criterion is based on the “jackknife” methodology. Examples are presented to illustrate implementation of the proposed criterion.
AB - There are numerous variable selection rules in classical discriminant analysis. These rules enable a researcher to distinguish significant variables from nonsignificant ones and thus provide a parsimonious classification model based solely on significant variables. Prominent among such rules are the forward and backward stepwise variable selection criteria employed in statistical software packages such as Statistical Package for the Social Sciences and BMDP Statistical Software. No such criterion currently exists for linear programming (LP) approaches to discriminant analysis. In this paper, a criterion is developed to distinguish significant from nonsignificant variables for use in LP models. This criterion is based on the “jackknife” methodology. Examples are presented to illustrate implementation of the proposed criterion.
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UR - http://www.scopus.com/inward/citedby.url?scp=84985817878&partnerID=8YFLogxK
U2 - 10.1111/j.1540-5915.1988.tb00286.x
DO - 10.1111/j.1540-5915.1988.tb00286.x
M3 - Article
VL - 19
SP - 554
EP - 563
JO - Decision Sciences
JF - Decision Sciences
SN - 0011-7315
IS - 3
ER -