PLS regression: a directional signal-to-noise ratio approach

DOI10.1016/j.jmva.2005.06.009zbMath1099.62070OpenAlexW2041269784MaRDI QIDQ2507757

Alain Mom, Pierre Druilhet

Publication date: 5 October 2006

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2005.06.009

zbMATH Keywords

partial least squaresprincipal componentsshrinkagebiased regressionregression on componentsconstrained least squares

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items

Maximum Likelihood Estimators in a Two Step Model for PLS ⋮ Characterization of estimators uniformly shrinking on subspaces ⋮ Shrinkage structure in biased regression ⋮ Spectral preconditioning of Krylov spaces: combining PLS and PC regression

Cites Work

• Projections under seminorms and generalized Moore Penrose inverses
• Partial least squares algorithm yields shrinkage estimators
• Shrinkage Structure of Partial Least Squares
• On the structure of partial least squares regression
• Comparison of Prediction Methods When Only a Few Components are Relevant
• The Peculiar Shrinkage Properties of Partial Least Squares Regression
• A Statistical View of Some Chemometrics Regression Tools
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