Regression of a data matrix on descriptors of both its rows and of its columns via latent variables: L-PLSR

DOI10.1016/j.csda.2003.10.004zbMath1429.62027OpenAlexW2036965437MaRDI QIDQ957091

Magni Martens, Harald Martens, Frank Westad, Anette Thybo, Lars Halvor Gidskehaug, Arnar Flatberg, Endre Anderssen, Martin Høy

Publication date: 26 November 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2003.10.004

zbMATH Keywords

singular value decompositioneigenvectorpartial least squaresSVDlatent variablesregressionPLSmultivariatebi-linear modelbooksteinL-PLSRPLSRthree-block

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Multivariate analysis (62H99)

Related Items (3)

Factorial modelling of the interactions between two sets of observations: the PLS-FILM method ⋮ Two-step PLS regression for L-structured data: an application in the cosmetic industry ⋮ On the use of quantile regression to deal with heterogeneity: the case of multi-block data

Cites Work

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• Partial least-squares vs. Lanczos bidiagonalization. I: Analysis of a projection method for multiple regression
• On partial least squares dimension reduction for microarray-based classification: a simulation study

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