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 | |
| Publication status | Published - 2009 Jun 1 |
| Externally published | Yes |
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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
- Applied Mathematics
- Control and Optimization
Cite this
Choosing the best set of variables in regression analysis using integer programming. / Konno, Hiroshi; Yamamoto, Rei.
In: Journal of Global Optimization, Vol. 44, No. 2, 01.06.2009, p. 273-282.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Choosing the best set of variables in regression analysis using integer programming
AU - Konno, Hiroshi
AU - Yamamoto, Rei
PY - 2009/6/1
Y1 - 2009/6/1
N2 - 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.
AB - 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.
KW - 0-1 integer programming
KW - Cardinality constraint
KW - Least absolute deviation
KW - Linear regression
KW - Variable selection
UR - http://www.scopus.com/inward/record.url?scp=67349161084&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67349161084&partnerID=8YFLogxK
U2 - 10.1007/s10898-008-9323-9
DO - 10.1007/s10898-008-9323-9
M3 - Article
AN - SCOPUS:67349161084
VL - 44
SP - 273
EP - 282
JO - Journal of Global Optimization
JF - Journal of Global Optimization
SN - 0925-5001
IS - 2
ER -