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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
Published by: Wiley for the Royal Statistical Society
DOI: 10.2307/2346776
Stable URL: http://www.jstor.org/stable/2346776
Page Count: 12
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
On the Investigation of Alternative Regressions by Principal Component Analysis
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Abstract

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.

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