A Variable Selection Criterion in the Linear Programming Approaches to Discriminant Analysis

Ravinder Nath, Thomas W. Jones

Research output: Contribution to journalArticle

18 Scopus citations

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 languageEnglish (US)
Pages (from-to)554-563
Number of pages10
JournalDecision Sciences
Volume19
Issue number3
DOIs
StatePublished - Sep 1988

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting(all)
  • Strategy and Management
  • Information Systems and Management
  • Management of Technology and Innovation

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