Multigroup Discriminant Analysis Using Linear Programming

Willy Gochet, Antonie Stam, V. Srinivasan and Shaoxiang Chen
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Willy Gochet: Katholieke Universiteit Leuven, Leuven, Belgium
Antonie Stam: The University of Georgia, Athens, Georgia, and International Institute for Applied Systems Analysis, Laxenburg, Austria
V. Srinivasan: Stanford University, Stanford, California
Shaoxiang Chen: Nanyang Technological University, Singapore

Operations Research, 1997, vol. 45, issue 2, 213-225

Abstract: In this paper we introduce a nonparametric linear programming formulation for the general multigroup classification problem. Previous research using linear programming formulations has either been limited to the two-group case, or required complicated constraints and many zero-one variables. We develop general properties of our multigroup formulation and illustrate its use with several small example problems and previously published real data sets. A comparative analysis on the real data sets shows that our formulation may offer an interesting robust alternative to parametric statistical formulations for the multigroup discriminant problem.

Keywords: programming; linear applications; statistics; nonparametric; discriminant analysis (search for similar items in EconPapers)
Date: 1997
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Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:45:y:1997:i:2:p:213-225

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