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 › peer-review

23 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
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

Access to Document

10.1007/s10898-008-9323-9

Fingerprint Dive into the research topics of 'Choosing the best set of variables in regression analysis using integer programming'. Together they form a unique fingerprint.

Cite this

@article{38367c24c84244318bf85d6db4b53e98,

title = "Choosing the best set of variables in regression analysis using integer programming",

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.",

keywords = "0-1 integer programming, Cardinality constraint, Least absolute deviation, Linear regression, Variable selection",

author = "Hiroshi Konno and Rei Yamamoto",

note = "Funding Information: Acknowledgements The research of the first author has been partly supported by the Grant-in-Aid for Scientific Research B18310109 of MEXT of the Government of Japan.",

year = "2009",

month = jun,

doi = "10.1007/s10898-008-9323-9",

language = "English",

volume = "44",

pages = "273--282",

journal = "Journal of Global Optimization",

issn = "0925-5001",

publisher = "Springer Netherlands",

number = "2",

}

TY - JOUR

T1 - Choosing the best set of variables in regression analysis using integer programming

AU - Konno, Hiroshi

AU - Yamamoto, Rei

N1 - Funding Information: Acknowledgements The research of the first author has been partly supported by the Grant-in-Aid for Scientific Research B18310109 of MEXT of the Government of Japan.

PY - 2009/6

Y1 - 2009/6

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 -

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

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Cite this