A comparison of various methods for multivariate regression with highly collinear variables

DOI10.1007/s10260-006-0025-5zbMath1405.62096OpenAlexW2123201958MaRDI QIDQ635880

Henk A. L. Kiers, Age K. Smilde

Publication date: 25 August 2011

Published in: Statistical Methods and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10260-006-0025-5

zbMATH Keywords

multicollinearityPLSmultivariate regressionprincipal component regressionpower regressionprincipal covariate regression

Mathematics Subject Classification ID

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

Related Items (4)

A systematic study into the factors that affect the predictive accuracy of multilevel VAR(1) models ⋮ Number of predictors and multicollinearity: What are their effects on error and bias in regression? ⋮ Principal covariates clusterwise regression (PCCR): accounting for multicollinearity and population heterogeneity in hierarchically organized data ⋮ Confronting collinearity in environmental regression models: evidence from world data

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Majorization as a tool for optimizing a class of matrix functions
• Reduced-rank regression for the multivariate linear model
• Redundancy analysis: an alternative for canonical correlation analysis
• The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses
• Joint Continuum Regression for Multiple Predictands
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• The elements of statistical learning. Data mining, inference, and prediction

This page was built for publication: A comparison of various methods for multivariate regression with highly collinear variables