Mixed integer second-order cone programming formulations for variable selection in linear regression

•AIC/BIC minimization, and adjusted R2 maximization problems are considered.•These problems are formulated as mixed integer second-order cone programming problems.•Experiments shows results of better quality than obtained by stepwise regression. This study concerns a method of selecting the best sub...

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Vydáno v:European journal of operational research Ročník 247; číslo 3; s. 721 - 731
Hlavní autoři: Miyashiro, Ryuhei, Takano, Yuichi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 16.12.2015
Elsevier Sequoia S.A
Témata:
Decision making models
Information criterion
Integer programming
Mathematical problems
Multiple linear regression
Regression analysis
Second-order cone programming
Studies
Variable selection
Variables
Integer programming
Multiple linear regression
Information criterion
Second-order cone programming
Variable selection
ISSN:0377-2217, 1872-6860
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Shrnutí:•AIC/BIC minimization, and adjusted R2 maximization problems are considered.•These problems are formulated as mixed integer second-order cone programming problems.•Experiments shows results of better quality than obtained by stepwise regression. This study concerns a method of selecting the best subset of explanatory variables in a multiple linear regression model. Goodness-of-fit measures, for example, adjusted R2, AIC, and BIC, are generally used to evaluate a subset regression model. Although variable selection with regard to these measures is usually performed with a stepwise regression method, it does not always provide the best subset of explanatory variables. In this paper, we propose mixed integer second-order cone programming formulations for selecting the best subset of variables with respect to adjusted R2, AIC, and BIC. Computational experiments show that, in terms of these measures, the proposed formulations yield better solutions than those provided by common stepwise regression methods.
Bibliografie:SourceType-Scholarly Journals-1
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content type line 14
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2015.06.081