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Selecting principal components in regression

Author

Listed:
  • Mason, Robert L.
  • Gunst, Richard F.

Abstract

Criteria for the deletion of principal components in regression are usually based on one of two indicators of components effects: (i) the magnitude of the eigenvalues of the predictor-variable correlation matrix or (ii) statistical tests of the significance of the components. Advocates of the first criterion cite guaranteed variance reduction properties as a rational for their proposals whereas proponents of inferential criteria point out that deletion solely on the basis of the magnitude of the eigenvalues ignores the potentials for bias. In this note we discuss the liminations of the second approach.

Suggested Citation

  • Mason, Robert L. & Gunst, Richard F., 1985. "Selecting principal components in regression," Statistics & Probability Letters, Elsevier, vol. 3(6), pages 299-301, October.
    • Handle: RePEc:eee:stapro:v:3:y:1985:i:6:p:299-301
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    Cited by:

    1. Elkin Castaño & Santiago Gallón, 2017. "A solution for multicollinearity in stochastic frontier production function models," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 86, pages 9-23, Enero - J.
    2. Heni Masruroh & Soemarno Soemarno & Syahrul Kurniawan & Amin Setyo Leksono, 2023. "A Spatial Model of Landslides with A Micro-Topography and Vegetation Approach for Sustainable Land Management in the Volcanic Area," Sustainability, MDPI, vol. 15(4), pages 1-26, February.
    3. Shuangge Ma & Michael R. Kosorok & Jason P. Fine, 2006. "Additive Risk Models for Survival Data with High-Dimensional Covariates," Biometrics, The International Biometric Society, vol. 62(1), pages 202-210, March.
    4. Castano, Elkin & Gallon, Santiago, 2016. "A solution for multicollinearity in stochastic frontier production function models," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue 86, pages 9-23, December.

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