Subset selection in multiple linear regression: a new mathematical programming approach

Authors:
Burak Eksioglu;Riza Demirer;Ismail Capar
Affiliations:
Department of Industrial Engineering, Mississippi State University, Mississippi State, MS 39762, USA;Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026, USA;Department of Industrial Engineering, Mississippi State University, Mississippi State, MS 39762, USA
Venue:
Computers and Industrial Engineering
Year:
2005

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Abstract

A new mathematical programming model is proposed to address the subset selection problem in multiple linear regression where the objective is to select a minimal subset of predictor variables without sacrificing any explanatory power. A parametric solution of this model yields a number of efficient subsets. To obtain this solution, an optimal or one of two heuristic algorithms is repeatedly used. The subsets generated are compared to ones generated by several standard procedures. The results suggest that the new approach finds subsets that compare favorably against the standard procedures in terms of the generally accepted measure: adjusted R^2.