Mitigating collinearity in linear regression models using ridge, surrogate and raised estimators

DOI10.1080/23311835.2016.1144697zbMath1426.62205OpenAlexW2287702541MaRDI QIDQ4966735

Donald E. Ramirez, Diarmuid O'Driscoll

Publication date: 27 June 2019

Published in: Cogent Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/23311835.2016.1144697

zbMATH Keywords

collinearityridge estimatorsraise estimatorssurrogate estimators

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Cites Work

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• Surrogate models in ill-conditioned systems
• Revision: variance inflation in regression
• Response surface designs using the generalized variance inflation factors
• Anomalies in the Foundations of Ridge Regression
• Solving Systems of Linear Equations With a Positive Definite, Symmetric, but Possibly Ill-Conditioned Matrix
• An Algorithm for Least-Squares Estimation of Nonlinear Parameters
• Ridge Regression in Practice
• The Early Use of Matrix Diagonal Increments in Statistical Problems
• The successive raising estimator and its relation with the ridge estimator
• Collinearity: revisiting the variance inflation factor in ridge regression
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation
• A method for the solution of certain non-linear problems in least squares

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