A decision rule for discarding principal components in regression

DOI10.1016/S0378-3758(99)00216-5zbMath0954.62081MaRDI QIDQ1582370

Thierry Foucart

Publication date: 18 February 2001

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

zbMATH Keywords

linear modelF-testcollinearitytype 1 and 2 error probabilitiesunbiased residual variance

Mathematics Subject Classification ID

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

Related Items (3)

Simultaneous dimension reduction and variable selection in modeling high dimensional data ⋮ Discussion of different logistic models with functional data. Application to systemic lupus erythematosus ⋮ Unnamed Item

Cites Work

• The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Ridge Regression: Applications to Nonorthogonal Problems
• Some Comments on C P
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