Principal component regression

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In statistics, principal component regression (PCR) is a regression analysis that uses principal component analysis when estimating regression coefficients.

In PCR instead of regressing the independent variables (the regressors) on the dependent variable directly, the principal components of the independent variables are used. One typically only uses a subset of the principal components in the regression, making a kind of regularized estimation. Often the principal components with the highest variance are selected. However, the low-variance principal components may also be important, — in some cases even more important.^[1]

[edit] See also

• Canonical correlation
• Partial least squares regression
• Total sum of squares

[edit] References