Variance Inflation Factor and Condition Number in multiple linear regression

DOI10.1080/00949655.2018.1463376OpenAlexW2799871693WikidataQ129947856 ScholiaQ129947856MaRDI QIDQ4960691

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Publication date: 23 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2018.1463376

zbMATH Keywords

condition numberdata transformationvariance inflation factormulticollinearity detection

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (6)

Choice of the ridge factor from the correlation matrix determinant ⋮ MCVIS: A New Framework for Collinearity Discovery, Diagnostic, and Visualization ⋮ Obtaining a threshold for the Stewart index and its extension to ridge regression ⋮ Fast feature selection via streamwise procedure for massive data ⋮ Residualization: justification, properties and application ⋮ Comment on “A Note on Collinearity Diagnostics and Centering” by Velilla (2018)

Cites Work

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• Transformation of variables and the condition number in ridge estimation
• Collinearity and least squares regression
• The problem of near-multicollinearity revisited: erratic vs systematic volatility.
• A Simulation Study of Some Ridge Estimators
• On the Distribution of a Scaled Condition Number
• A Monte Carlo Evaluation of Some Ridge-Type Estimators
• Tolerance and Condition in Regression Computations
• Collinearity diagnostic applied in ridge estimation through the variance inflation factor
• Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation
• Standardization of Variables and Collinearity Diagnostic in Ridge Regression

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