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Mixed Integer Quadratic Optimization Formulations for Eliminating Multicollinearity Based on Variance Inflation Factor

Ryuta Tamura (s154558y***at***st.go.tuat.ac.jp)
Ken Kobayashi (ken-kobayashi***at***jp.fujitsu.com)
Yuichi Takano (ytakano***at***isc.senshu-u.ac.jp)
Ryuhei Miyashiro (r-miya***at***cc.tuat.ac.jp)
Kazuhide Nakata (nakata.k.ac***at***m.titech.ac.jp)
Tomomi Matsui (matsui.t.af***at***m.titech.ac.jp)

Abstract: The variance inflation factor, VIF, is the most frequently used indicator for detecting multicollinearity in multiple linear regression models. This paper proposes two mixed integer quadratic optimization formulations for selecting the best subset of explanatory variables under upper-bound constraints on VIF of selected variables. Computational results illustrate the effectiveness of our optimization formulations based on comparisons with conventional local search algorithms.

Keywords: Integer programming, subset selection, multicollinearity, variance inflation factor, multiple linear regression, statistics

Category 1: Applications -- Science and Engineering

Category 2: Applications -- Science and Engineering (Statistics )

Category 3: Integer Programming


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Entry Submitted: 09/29/2016
Entry Accepted: 09/29/2016
Entry Last Modified: 12/02/2016

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