Choosing the best set of variables in regression analysis using integer programming

Hiroshi Konno; Rei Yamamoto

doi:10.1007/s10898-008-9323-9

Choosing the best set of variables in regression analysis using integer programming

Hiroshi Konno, Rei Yamamoto

Research output: Contribution to journal › Article

19 Citations (Scopus)

Abstract

This paper is concerned with an algorithm for selecting the best set of s variables out of k(> s) candidate variables in a multiple linear regression model. We employ absolute deviation as the measure of deviation and solve the resulting optimization problem by using 0-1 integer programming methodologies. In addition, we will propose a heuristic algorithm to obtain a close to optimal set of variables in terms of squared deviation. Computational results show that this method is practical and reliable for determining the best set of variables.

Original language	English
Pages (from-to)	273-282
Number of pages	10
Journal	Journal of Global Optimization
Volume	44
Issue number	2
DOIs	https://doi.org/10.1007/s10898-008-9323-9
Publication status	Published - 2009 Jun 1
Externally published	Yes

Keywords

0-1 integer programming
Cardinality constraint
Least absolute deviation
Linear regression
Variable selection

ASJC Scopus subject areas

Computer Science Applications
Management Science and Operations Research
Control and Optimization
Applied Mathematics

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Konno, H., & Yamamoto, R. (2009). Choosing the best set of variables in regression analysis using integer programming. Journal of Global Optimization, 44(2), 273-282. https://doi.org/10.1007/s10898-008-9323-9

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