A Comparison of the Classification Accuracy of Linear and Quadratic Statistical Discriminant Models versus Linear and Quadratic Programming Discriminant Models

<p>The purpose of this research was to compare the classification accuracy of two mathematical programming models versus traditional statistical discriminant analysis. Monte Carlo techniques were used to compute population 1, population 2, and average misclassification rates for the linear discriminant function (LDF), the quadratic discriminant function (QDF), a linear programming discriminant model (LPDM), and a quadratic programming discriminant model (QPDM) for specific values of several parameters which affect discriminant analysis. This study was restricted to the two group, two variable discriminant problem.</p>

Name thesis:degree_name

Doctor of Philosophy in Business Administration (PhD)

Level thesis:degree_level

Dissertation

Year dc:date.available

1986

Author dc:creator

• Ferry, Jerry William

Advisor dc:contributor.advisor

• Jones, Thomas W.

Contributors dc:contributor

• Douglas, David E.
• Williams, Nolan E.

• Linear programming model
• quadratic programming model
• Monte Carlo techniques
• Accounting
• Business Administration, Management, and Operations
• Finance and Financial Management

Repository record dc:identifier

https://scholarworks.uark.edu/etd/3438

OAI identifier oai:identifier

oai:scholarworks.uark.edu:etd-4988