Regresion analysis with multicollinear predictor variables: definition, derection, and effects

DOI10.1080/03610928308828603zbMath0581.62058OpenAlexW2003287964MaRDI QIDQ3703133

Richard F. Gunst

Publication date: 1983

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610928308828603

zbMATH Keywords

surveyoutliersregression analysisill-conditioningrobust estimationBayesian modelingleast squares estimatorsbiased estimationmulticollinearity among predictor variables

Mathematics Subject Classification ID

Linear inference, regression (62J99)

Related Items (14)

Selecting the optimum k in ridge regression ⋮ Collinearity detection in linear regression models ⋮ Latent root regression: a biased regression methodology for use with collinear predictor variables ⋮ On eigenvalues, case deletion and extremes in regression ⋮ A note on the behaviour of augmented principal-component plots in regression ⋮ Predictability measures for ridge regression models ⋮ Detection of collinearity- influential observations ⋮ Collinearity in generalized linear models ⋮ Handling multicollinearity in quantile regression through the use of principal component regression ⋮ Revision: variance inflation in regression ⋮ On the potential in the estimation of linear functions in regression ⋮ Una caracterizacion aproximada de casos influyentes en multicolinealidad ⋮ Bayesian Estimation of the Log–linear Exponential Regression Model with Censorship and Collinearity ⋮ Ill-conditioning and multicollinearity

Cites Work

• Bounded-leverage weights for robust regression estimators
• Augmented Robust Estimators
• Building Multiple Regression Models Interactively
• A Biometrics Invited Paper. The Analysis and Selection of Variables in Linear Regression
• Advantages of Examining Multicollinearities in Regression Analysis
• Bayes's theorem and the use of prior knowledge in regression analysis
• Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation
• Robust Statistics
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