title: How to avoid over-fitting in multivariate calibration--the conventional validation approach and an alternative.
abstract: 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 spectral data set using partial least squares (PLS) regression. The results indicate that the alternative approach is more objective, since, unlike the validation-based approach, it does not require the use of 'soft' decision rules. The alternative approach therefore appears to be a useful addition to the chemometrician's toolbox.
authors.affiliation: Chemometry Consultancy, Rubensstraat 7, 6717 VD Ede, The Netherlands. nmf@chemometry.com
authors.forename: N M
authors.initials: NM
authors.lastname: Faber
authors.forename: R
authors.initials: R
authors.lastname: Rajkó
mesh_terms: D002138:Calibration / Q000592:standards*; D015999:Multivariate Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity