Dimension reduction via principal variables

DOI10.1016/J.CSDA.2007.02.012zbMath1452.62408OpenAlexW2133899637MaRDI QIDQ1020840

Jonathan A. Cumming, David A. Wooff

Publication date: 2 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://dro.dur.ac.uk/3106/1/3106.pdf

zbMATH Keywords

utilitylongitudinal datarepeated measuresprincipal componentsvariable selectionpartial covariancepartial correlation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (3)

Challenging the curse of dimensionality in multivariate local linear regression ⋮ Editorial: Statistical learning methods including dimensionality reduction ⋮ Intermediate Variable Emulation: Using Internal Processes in Simulators to Build More Informative Emulators

Uses Software

• fda (R)
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Cites Work

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• How many principal components? Stopping rules for determining the number of non-trivial axes revisited
• Determining the number of components from the matrix of partial correlations
• Applied functional data analysis. Methods and case studies
• Principal component analysis.
• Principal Variables
• Un critère de choix de variables en analyse en composantes principales fondé sur des modèles graphiques gaussiens particuliers
• VARIABLE SELECTION AND INTERPRETATION OF COVARIANCE PRINCIPAL COMPONENTS
• Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition

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