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On the Investigation of Alternative Regressions by Principal Component Analysis
Douglas M. Hawkins
Journal of the Royal Statistical Society. Series C (Applied Statistics)
Vol. 22, No. 3 (1973), pp. 275-286
Stable URL: http://www.jstor.org/stable/2346776
Page Count: 12
You can always find the topics here!Topics: Statistical variance, Hyperplanes, Eigenvectors, Multiple regression, Linear regression, Mathematical dependent variables, Applied statistics, Eigenvalues, Mathematical problems, Mathematical vectors
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In a multiple regression problem, let the p × 1 vector x consist of the dependent variable and p - 1 predictor variables. The correlation matrix of x is reduced to principal components. The components corresponding to low eigenvalues may be useful in suggesting possible alternative subregressions. This possibility is analysed, and formulae derived for the derivation of subregressions from the principal components.
Journal of the Royal Statistical Society. Series C (Applied Statistics) © 1973 Royal Statistical Society