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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Ferry, Jerry William"],"institution":null,"degree_name":"Doctor of Philosophy in Business Administration (PhD)","degree_level":"Dissertation","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Douglas, David E.","Williams, Nolan E."],"advisors":["Jones, Thomas W."],"committee_chairs":[],"committee_members":[],"year":1986,"date_issued":"1986-08-01T07:00:00Z","date_published":"1986-08-01T07:00:00Z","updated_at":"2026-07-24T00:59:08Z","subjects":["Linear programming model","quadratic programming model","Monte Carlo techniques","Accounting","Business Administration, Management, and Operations","Finance and Financial Management"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uark.edu/etd/3438","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Douglas, David E.","Williams, Nolan E."]},{"key":"dc:contributor.advisor","label":"Advisor","values":["Jones, Thomas W."]},{"key":"dc:creator","label":"Author","values":["Ferry, Jerry William"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["1986"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-01-08T08:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy in Business Administration (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Linear programming model","quadratic programming model","Monte Carlo techniques","Accounting","Business Administration, Management, and Operations","Finance and Financial Management"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uark.edu/etd/3438"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["A Comparison of the Classification Accuracy of Linear and Quadratic Statistical Discriminant Models versus Linear and Quadratic Programming Discriminant Models"]}]}],"canonical_facts":{"dc:contributor":["Douglas, David E.","Williams, Nolan E."],"dc:contributor.advisor":["Jones, Thomas W."],"dc:creator":["Ferry, Jerry William"],"dc:date":["1986"],"dc:date.available":["2020-01-08T08:00:00Z"],"dc:description.abstract":["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.
"],"dc:identifier":["https://scholarworks.uark.edu/etd/3438"],"dc:subject":["Linear programming model","quadratic programming model","Monte Carlo techniques","Accounting","Business Administration, Management, and Operations","Finance and Financial Management"],"dc:title":["A Comparison of the Classification Accuracy of Linear and Quadratic Statistical Discriminant Models versus Linear and Quadratic Programming Discriminant Models"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy in Business Administration (PhD)"]},"updated_at":"2026-07-24T00:59:08Z"}