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**An interpretation of partial least squares.**
*(English)*
Zbl 0793.62034

Summary: Univariate partial least squares (PLS) is a method of modeling relationships between a \(Y\) variable and other explanatory variables. It may be used with any number of explanatory variables, even far more than the number of observations. A simple interpretation is given that shows the method to be a straightforward and reasonable way of forming prediction equations. Its relationship to multivariate PLS, in which there are two or more \(Y\) variables, is examined, and an example is given in which it is compared by simulation with other methods of forming prediction equations. With univariate PLS, linear combinations of the explanatory variables are formed sequentially and related to \(Y\) by ordinary least squares regression. It is shown that these linear combinations, here called components, may be viewed as weighted averages of predictors, where each predictor holds the residual information in an explanatory variable that is not contained in earlier components, and the quantity to be predicted is the vector of residuals from regressing \(Y\) against earlier components.

A similar strategy is shown to underlie multivariate PLS, except that the quantity to be predicted is a weighted average of the residuals from separately regressing each \(Y\) variable against earlier components. This clarifies the differences between univariate and multivariate PLS, and it is argued that in most situations, the univariate method is likely to give the better prediction equations.

In the example using simulation, univariate PLS is compared with four other methods of forming prediction equations: ordinary least squares, forward variable selection, principal components regression, and a Stein shrinkage method. Results suggest that PLS is a useful method for forming prediction equations when there are a large number of explanatory variables, particularly when the random error variance is large.

A similar strategy is shown to underlie multivariate PLS, except that the quantity to be predicted is a weighted average of the residuals from separately regressing each \(Y\) variable against earlier components. This clarifies the differences between univariate and multivariate PLS, and it is argued that in most situations, the univariate method is likely to give the better prediction equations.

In the example using simulation, univariate PLS is compared with four other methods of forming prediction equations: ordinary least squares, forward variable selection, principal components regression, and a Stein shrinkage method. Results suggest that PLS is a useful method for forming prediction equations when there are a large number of explanatory variables, particularly when the random error variance is large.

### MSC:

62J05 | Linear regression; mixed models |

62H25 | Factor analysis and principal components; correspondence analysis |

62J99 | Linear inference, regression |