MCVIS: A New Framework for Collinearity Discovery, Diagnostic, and Visualization
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Publication:5066423
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
Related Items (1)
Uses Software
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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