Technometrics · 1970 · 2.6K citations · 6 references
Natural SciencesBiostatisticsEconometricsStatistical ModelingTime Series AnalysisData AnalysisDimensionality ReductionMatrix ComputationMultivariate AnalysisNumerical AnalysisRegressionLeast Squares EstimationLeast Squares ProblemsOrdinary Least SquaresRidge Regression
This paper reviews ridge regression methods. It recommends strategies to obtain regression equations superior to ordinary least squares. The authors illustrate the approach with two literature examples and emphasize the ridge trace, a two‑dimensional graphical tool for visualizing complex multifactor relationships. The article is cited by Ridge Regression: A Historical Context.
Abstract This paper is an exposition of the use of ridge regression methods. Two examples from the literature are used as a base. Attention is focused on the RIDGE TRACE which is a two-dimensional graphical procedure for portraying the complex relationships in multifactor data. Recommendations are made for obtaining a better regression equation than that given by ordinary least squares estimation. This article is referred to by:Ridge Regression: A Historical Context
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Ridge Regression: Biased Estimation for Nonorthogonal Problems
Arthur E. Hoerl, Robert W. Kennard · Technometrics · 2000 · 6.6K citations
Selection of the Best Subset in Regression Analysis
R. R. Hocking, R. N. Leslie · Technometrics · 1967 · 285 citations
Selection of Variables for Fitting Equations to Data
John W. Gorman, R. J. Toman · Technometrics · 1966 · 274 citations
Selection of the Best Subset in Regression Analysis
R. R. Hocking, R. N. Leslie · Technometrics · 1967 · 77 citations