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

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 R2. 2005 Elsevier Ltd. All rights reserved.

authors

• Capar, Ismail

published proceedings

• Computers & Industrial Engineering

author list (cited authors)

• Eksioglu, B., Demirer, R., & Capar, I.

citation count

• 19

complete list of authors

• Eksioglu, Burak||Demirer, Riza||Capar, Ismail

publication date

• January 2005

publisher

• Elsevier  Publisher

published in

• Computers and Industrial Engineering  Journal

Digital Object Identifier (DOI)

• 10.1016/j.cie.2005.03.004

start page

• 155

end page

• 167

volume

• 49

issue

• 1

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.cie.2005.03.004