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- W2064739916 abstract "In this paper, penalized regression using the L1 norm on the estimated parameters is proposed for chemometric calibration. The algorithm is of the lasso type, introduced by Tibshirani in 1996 as a linear regression method with bound on the absolute length of the parameters, but a modification is suggested to cope with the singular design matrix most often seen in chemometric calibration. Furthermore, the proposed algorithm may be generalized to all convex norms like ∑|β j|γ where γ ≥ 1, i.e. a method that continuously varies from ridge regression to the lasso. The lasso is applied both directly as a calibration method and as a method to select important variables/wavelengths. It is demonstrated that the lasso algorithm, in general, leads to parameter estimates of which some are zero while others are quite large (compared to e.g. the traditional PLS or RR estimates). By using several benchmark data sets, it is shown that both the direct lasso method and the regression where the lasso acts as a wavelength selection method most often outperform the PLS and RR methods. Copyright © 2001 John Wiley & Sons, Ltd." @default.
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- W2064739916 date "2001-07-01" @default.
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- W2064739916 title "Calibration with absolute shrinkage" @default.
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- W2064739916 doi "https://doi.org/10.1002/cem.635.abs" @default.
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