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Principal Components Regression to Mitigate the Effects of Multicollinearity

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One consequence of multicollinearity among the structural independent variables of a regression model is that variables are frequently deleted as a means of proceeding with sensible hypothesis tests. Principal components regression has the advantage of avoiding model specification error due to variable deletion. The technique works as follows: the independent variables are orthogonalized into their principal components; components with low information content are deleted; the model is estimated by ordinary least squares; then the principal component estimators are converted into coefficients in the original parameter space, where a judgement about their contribution is made via an F-test. An example using tree growth data is presented to demonstrate the merits of principal components regression over variable deletion. Results indicate that "correct" structural specification does not have to be compromised through variable deletion when collinearity is present. This obviously has implications for large-scaled regression models, in which an increased number of independent variables in the specification may promote a level of collinearity that is not conducive to making statistical inferences. Analytical methods like principal components, that adjust for the effects of collinearity on the variable selection process, are merited. For. Sci. 37(1):191-199.
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Keywords: Restrictions; eigenvectors; orthogonality; sequential deletion

Document Type: Journal Article

Affiliations: Research Soil Scientist, USDA Forest Service, Southeastern Forest Experiment Station, Research Triangle Park, NC 27709

Publication date: 1991-03-01

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  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
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