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In the multivariate calibration framework we revisit and investigate the prediction performance of three high‐dimensional modeling strategies: partial least squares, principal component regression and P‐spline signal regression. Specifically we are interested in comparing the stability and robustness of prediction under differing conditions, e.g. training the model under one temperature and using it to predict under differing temperatures. An example illustrates stability comparisons.
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Marx et al. (2002) studied this question.
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