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Do not adjust coefficients in Shapley value regression

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  • Ulrike Grömping
  • Sabine Landau

Abstract

Shapley value regression consists of assessing relative importance and accordingly adjusting regression coefficients. It is argued that adjustment of coefficients is unnecessary and even misleading for practically relevant situations. Examples are given, and an alternative procedure is proposed for situations for which the coefficients are requested to have a certain sign. Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • Ulrike Grömping & Sabine Landau, 2010. "Do not adjust coefficients in Shapley value regression," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(2), pages 194-202, March.
    • Handle: RePEc:wly:apsmbi:v:26:y:2010:i:2:p:194-202
    DOI: 10.1002/asmb.773
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    References listed on IDEAS

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    2. Dorfman, Jeffrey H. & McIntosh, Christopher S., 2001. "Imposing inequality restrictions: efficiency gains from economic theory," Economics Letters, Elsevier, vol. 71(2), pages 205-209, May.
    3. Thomson, Michael & Schmidt, Peter, 1982. "A Note on the Comparison of the Mean Square Error of Inequality Constrained Least Squares and Other Related Estimators," The Review of Economics and Statistics, MIT Press, vol. 64(1), pages 174-176, February.
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    Cited by:

    1. Miklós Pintér, 2011. "Regression games," Annals of Operations Research, Springer, vol. 186(1), pages 263-274, June.
    2. Riccardo Colini-Baldeschi & Marco Scarsini & Stefano Vaccari, 2018. "Variance Allocation and Shapley Value," Methodology and Computing in Applied Probability, Springer, vol. 20(3), pages 919-933, September.

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