Abstract
PLS (Partial Least Squares) regression is a model for situations where a low observation/variable ratio comes with highly collinear predictors. A comparison with other statistical methods can be found in Frank and Friedman (1993). The PLS method, very popular in chemometrics, has been generalized in several ways in order to extend PLS into nonlinearity. The first one (Wold et al., 1989) replaces the standard regression of the Y response matrix on the latent variable t with a quadratic model. The second one (Frank, 1990) uses a smooth nonlinear function of the latent variable through the SMART regression (Friedman, 1984).
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© 1994 Springer-Verlag Berlin Heidelberg
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Durand, JF., Sabatier, R. (1994). PLS Regression via Additive Splines. In: Dutter, R., Grossmann, W. (eds) Compstat. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-52463-9_56
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DOI: https://doi.org/10.1007/978-3-642-52463-9_56
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-0793-6
Online ISBN: 978-3-642-52463-9
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