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Robust regression: an inferential method for determining which independent variables are most important

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  • Rand R. Wilcox

Abstract

Consider the usual linear regression model consisting of two or more explanatory variables. There are many methods aimed at indicating the relative importance of the explanatory variables. But in general these methods do not address a fundamental issue: when all of the explanatory variables are included in the model, how strong is the empirical evidence that the first explanatory variable is more or less important than the second explanatory variable? How strong is the empirical evidence that the first two explanatory variables are more important than the third explanatory variable? The paper suggests a robust method for dealing with these issues. The proposed technique is based on a particular version of explanatory power used in conjunction with a modification of the basic percentile method.

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  • Rand R. Wilcox, 2018. "Robust regression: an inferential method for determining which independent variables are most important," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 100-111, January.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:100-111
    DOI: 10.1080/02664763.2016.1268105
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    References listed on IDEAS

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    1. Bi, Jun & Wang, Yongxing & Sai, Qiuyue & Ding, Cong, 2019. "Estimating remaining driving range of battery electric vehicles based on real-world data: A case study of Beijing, China," Energy, Elsevier, vol. 169(C), pages 833-843.

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