Article ID: | iaor2006393 |
Country: | United Kingdom |
Volume: | 49 |
Issue: | 1 |
Start Page Number: | 155 |
End Page Number: | 167 |
Publication Date: | Aug 2005 |
Journal: | Computers & Industrial Engineering |
Authors: | Eksioglu Burak, Demirer Riza, Capar Ismail |
Keywords: | programming: mathematical, statistics: multivariate |
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