Tidsskrift : Journal of Near Infrared Spectroscopy , vol. 8 , p. 117–124 , 2000
Trykt : 0967-0335
Elektronisk : 1751-6552
Publikasjonstype : Vitenskapelig artikkel
Sak : 2
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A jack-knife based method for variable selection in partial least squares regression is presented. The method is based on significance tests of model parameters, in this paper applied to regression coefficients. The method is tested on a near infrared (NIR) spectral data set recorded on beer samples, correlated to extract concentration and compared to other methods with known merit. The results show that the jack-knife based variable selection performs as well or better than other variable selection methods do. Furthermore, results show that the method is robust towards various cross-validation schemes (the number of segments and how they are chosen).