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Contribution of individual variables to the regression sum of squares

Shabuz, Zillur R. and Garthwaite, Paul H. (2019). Contribution of individual variables to the regression sum of squares. Model Assisted Statistics and Applications, 14(4) pp. 281–296.

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In applications of multiple regression, one of the most common goals is to measure the relative importance of each predictor variable. If the predictors are uncorrelated, quantification of relative importance is simple and unique. However, in practice, predictor variables are typically correlated and there is no unique measure of a predictor variable’s relative importance. Using a transformation to orthogonality, new measures are constructed for evaluating the contribution of individual variables to a regression sum of squares. The transformation yields an orthogonal approximation of the columns of the predictor scores matrix and it maximizes the sum of the covariances between the cross-product of individual regressors and the response variable and the cross-product of the transformed orthogonal regressors and the response variable. The new measures are compared with three previously proposed measures through examples and the properties of the measures are examined.

Item Type: Journal Item
ISSN: 1875-9068
Keywords: Dominance analysis; orthogonal counterparts; relative importance; relative weights; rotation invariance; transformation to orthogonality
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 69135
SWORD Depositor: Jisc Publications-Router
Depositing User: Jisc Publications-Router
Date Deposited: 27 Jan 2020 09:25
Last Modified: 12 Jun 2020 13:14
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