Combining bilinear modelling and ridge regression
Publikasjonsdetaljer
Tidsskrift : Journal of Chemometrics , vol. 16 , p. 313–318 , 2002
Internasjonale standardnummer
:
Trykt
:
0886-9383
Elektronisk
:
1099-128X
Publikasjonstype : Vitenskapelig artikkel
Lenker
:
DOI
:
doi.org/10.1002/cem.727
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Kjetil Aune
Bibliotekleder
kjetil.aune@nofima.no
Sammendrag
A method is presented for making principal component regression (PCR), partial least squares regression (PLSR) and other regressions based on bilinear modelling (BLM) less sensitive to overfit. The idea is to use generalized ridge regression to calculate the Y-loadings in order to prevent small, uncertain values of the score vectors from causing inflation of variance in the regression coefficients. Thus we combine the stabilizing power of ridge regression with the modelling power and interpretability of bilinear models. The method is intended to provide better predictive ability and improved stability for regression models. Copyright (C) 2002 John Wiley Sons, Ltd.