The peculiar shrinkage properties of partial least squares regression

By N. Butler, M. C. Denham. 2000

74 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3ecad4aafacc64334140bb5ecca79f37f61dcdf2

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In Analytical Chemistry

In Statistics and Probability

Citations in the corpus

Using vegetation health indices and partial least squares method for estimation of corn yield (2008, unknown): “PLS methodology works by extracting successive linear combinations of the predictors or independent variables called factors, which explain both variation of the dependent and independent variables (Martens 1985, Garthwaite 1994, Goutis 1996, Butler and Denham 2000, Westad and Martens 2000).”
Overview and Recent Advances in Partial Least Squares (2005, background): “The shrinkage factors have some remarkable properties [7, 21].”
Some theoretical aspects of partial least squares regression (2001, background): “This suspicion has recently been confirmed by rigorous detailed mathematical calculations independently by Butler and Denham (2000) and by Lingjcerde and Christophersen (2000): The functions j(.”
Partial Least Squares (2005, methodology): “This shrinkage, due to the fact that PLS is an inverse calibration procedure, has been studied extensively by various authors [1,7,29,30].”
A method for screening active effects in supersaturated designs (2007, background): “Recent work focusing on this topic includes, e.g. Helland (1990), Garthwaite (1994), Butler and Denham (2000), Höskuldsson (2001) and Bastien et al. (2005).”
Sparse partial least squares regression for simultaneous dimension reduction and variable selection (2010, background): “It has been shown that pls yields shrinkage estimators (Goutis, 1996) and may provide peculiar shrinkage in the sense that some of the components of the regression coeﬃcient vector may expand (Butler and Denham, 2000).”
Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning (2008, background): “Linear Discriminant Analysis (76), vehicle windows float processed (17), containers (13), tableware (9), and headlamps (29).”