MCVIS: A New Framework for Collinearity Discovery, Diagnostic, and Visualization

DOI10.1080/10618600.2020.1779729OpenAlexW3039338374MaRDI QIDQ5066423

Chen Lin, Samuel Müller, Unnamed Author

Publication date: 29 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2020.1779729

zbMATH Keywords

resamplingmulticollinearityvariance inflation factorhigh correlations

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

mcvis

Uses Software

• R
• SenSrivastava
• mplot

Cites Work

• Collinearity and least squares regression
• Bootstrap Model Selection
• Variance Inflation Factor and Condition Number in multiple linear regression
• Collinearity diagnostic applied in ridge estimation through the variance inflation factor
• Model selection and parameter estimation in non-linear nested models: a sequential generalized DKL-optimum design
• Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation
• Outlier Robust Model Selection in Linear Regression
• Comment on “A Note on Collinearity Diagnostics and Centering” by Velilla (2018)
• A Note on Collinearity Diagnostics and Centering
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