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One of the main issues in principal component regression (PCR) and partial least squares regression (PLSR) is the selection of the number of principal components. To this end, the curve with the root mean squared error of cross-validated prediction (RMSECV) is often described in the literature as a very helpful graphical tool. In this paper, we focus on model selection for robust calibration methods...
This paper critically reviews the problem of over-fitting in multivariate calibration and the conventional validation-based approach to avoid it. It proposes a randomization test that enables one to assess the statistical significance of each component that enters the model. This alternative is compared with cross-validation and independent test set validation for the calibration of a near-infrared...
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