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- W2022270623 abstract "Criteria for the selection of principal components in regression are usually founded on one of two indicators of component effects: (i) Student's tests of the significance of the components or (ii) the magnitude of the eigenvalues of the explanatory variables correlation matrix. Deletion of a principal component can give a bias in the estimates (first criterion), but can also reduce unbiased residual variance (second criterion). In this article we propose a decision rule with respect to the partial correlation coefficient between the principal component and the explained variable whilst taking the previous criteria into account." @default.
- W2022270623 created "2016-06-24" @default.
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- W2022270623 date "2000-08-01" @default.
- W2022270623 modified "2023-09-27" @default.
- W2022270623 title "A decision rule for discarding principal components in regression" @default.
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- W2022270623 doi "https://doi.org/10.1016/s0378-3758(99)00216-5" @default.
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