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An Analytic Variable Selection Technique for Principal Component Regression

Author

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  • E. R. Mansfield
  • J. T. Webster
  • R. F. Gunst

Abstract

This paper presents an analytic technique for deleting predictor variables from a linear regression model when principal components of X'X are removed to adjust for multicollinearities in the data. The technique can be adapted to commonly used variable selection procedures such as backward elimination to eliminate redundant predictor variables without appreciably increasing the residual sum of squares. An analysis of the pitprop data of Jeffers (1967) is performed to illustrate the methods proposed in the paper.

Suggested Citation

  • E. R. Mansfield & J. T. Webster & R. F. Gunst, 1977. "An Analytic Variable Selection Technique for Principal Component Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 26(1), pages 34-40, March.
    • Handle: RePEc:bla:jorssc:v:26:y:1977:i:1:p:34-40
    DOI: 10.2307/2346865
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    Cited by:

    1. Saeid Eslamian & Mehdi Ghasemizadeh & Monireh Biabanaki & Mansoor Talebizadeh, 2010. "A Principal Component Regression Method for Estimating Low Flow Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2553-2566, September.
    2. Bauer, Jan O. & Drabant, Bernhard, 2023. "Regression based thresholds in principal loading analysis," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
    3. Yukio Sadahiro & Yan Wang, 2018. "Configuration of sample points for the reduction of multicollinearity in regression models with distance variables," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 61(2), pages 295-317, September.
    4. Bauer, Jan O. & Drabant, Bernhard, 2021. "Principal loading analysis," Journal of Multivariate Analysis, Elsevier, vol. 184(C).

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