Comparison of prediction methods for multicollinear data

DOI10.1080/03610918508812458zbMath0576.62072OpenAlexW2034362174MaRDI QIDQ3696335

Tormod Næs, Harald Martens

Publication date: 1985

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918508812458

zbMATH Keywords

partial least squaresprincipal component regressioncomparison of prediction methodsmulticollinear data

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Linear regression; mixed models (62J05) Linear inference, regression (62J99)

Related Items (11)

Kernel continuum regression ⋮ Treatments of non-metric variables in partial least squares and principal component analysis ⋮ A simulation study on comparison of prediction methods when only a few components are relevant ⋮ On the structure of partial least squares regression ⋮ A weighted view on the partial least-squares algorithm ⋮ Influence function analysis applied to partial least squares ⋮ A procedure for the clustering of cell wall mutants in the model plant Arabidopsis based on Fourier-transform infrared (FT-IR) spectroscopy ⋮ Derived components regression using the BACON algorithm ⋮ Partial least squares algorithm yields shrinkage estimators ⋮ Influence function analysis for partial least squares with uncorrelated components ⋮ The geometry of 2-block partial least squares regression

Cites Work

• Corrigenda to Leone, White, Narula and Narula
• The partial least squares-fix point method of estimating interdependent systems with latent variables
• Simultaneous Estimation of Parameters in Different Linear Models and Applications to Biometric Problems
• Unnamed Item

This page was built for publication: Comparison of prediction methods for multicollinear data