Component selection norms for principal components regression

DOI10.1080/03610927708827494zbMath0361.62041OpenAlexW2023147062MaRDI QIDQ4134726

Stanley R. Johnson, R. Carter Hill, Thomas B. Fomby

Publication date: 1977

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610927708827494

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Linear regression; mixed models (62J05)

Related Items

Principal components in econometrics ⋮ Improved estimation under collinearity and squared error loss ⋮ Improved prediction in the presence of multicollinearity

Cites Work

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• PRINCIPAL COMPONENTS AND THE PROBLEM OF MULTICOLLINEARITY(*)
• Wallace's Weak Mean Square Error Criterion for Testing Linear Restrictions in Regression: A Tighter Bound
• A Test of the Mean Square Error Criterion for Restrictions in Linear Regression
• Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation
• Multiple Comparison of Regression Functions
• Weaker Criteria and Tests for Linear Restrictions in Regression

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