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

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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.

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Chuo University, 2-13-27, Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan

    Hiroshi Konno & Rei Yamamoto

  2. Mitsubishi UFJ Trust Investment Technology Institute Co., Ltd, 2-5-6, Shiba, Minato-ku, Tokyo, 105-0014, Japan

    Rei Yamamoto

Authors
  1. Hiroshi Konno
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  2. Rei Yamamoto
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Correspondence to Rei Yamamoto.

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Konno, H., Yamamoto, R. Choosing the best set of variables in regression analysis using integer programming. J Glob Optim 44, 273–282 (2009). https://doi.org/10.1007/s10898-008-9323-9

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  • DOI: https://doi.org/10.1007/s10898-008-9323-9

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