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Principal component regression, ridge regression and ridge principal component regression in spectroscopy calibration
Author(s) -
Vigneau E.,
Devaux M. F.,
Qannari E. M.,
Robert P.
Publication year - 1997
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/(sici)1099-128x(199705)11:3<239::aid-cem470>3.0.co;2-a
Subject(s) - principal component regression , multicollinearity , principal component analysis , partial least squares regression , ridge , ordinary least squares , statistics , regression , mathematics , calibration , regression analysis , linear regression , robust regression , generalized least squares , geology , paleontology , estimator
Ridge regression (RR) and principal component regression (PCR) are two popular methods intended to overcome the problem of multicollinearity which arises with spectral data. The present study compares the performances of RR and PCR in addition to ordinary least squares (OLS) and partial least squares (PLS) on the basis of two data sets. An alternative procedure that combines both PCR and RR is also introduced and is shown to perform well. Furthermore, the performance of the combination of RR and PCR is stable in so far as sufficient information is taken into account. This result suggests discarding those components that are unquestionably identified as noise, when the ridge constant tackles the degeneracy caused by components with small variances.