Subset selection using the total least squares approach in collinearity problems with errors in the variables

DOI10.1016/0024-3795(87)90130-3zbMath0618.65148OpenAlexW2007434155MaRDI QIDQ1822478

Joos Vandewalle, Sabine Van Huffel

Publication date: 1987

Published in: Linear Algebra and its Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0024-3795(87)90130-3

zbMATH Keywords

predictionstabilityparameter estimationlinear modelcollinearity problemsubset selectiontotal least squaresresponse variableRidge regressionpredictor variablesshrinking factors

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Linear regression; mixed models (62J05) Probabilistic methods, stochastic differential equations (65C99)

Cites Work

• An Analysis of the Total Least Squares Problem
• Consistent estimation of a regression with errors in the variables
• Ridge Regression — 1980: Advances, Algorithms, and Applications
• Principal Variables
• Developments in Linear Regression Methodology: 1959-1982
• Selection of Variables in Multiple Regression: Part II. Chosen Procedures, Computations and Examples
• Ridge Regression and James-Stein Estimation: Review and Comments
• Collinearity and Optimal Restrictions on Regression Parameters for Estimating Responses
• Latent Root Regression Analysis
• A Biometrics Invited Paper. The Analysis and Selection of Variables in Linear Regression
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