Dimension reduction via principal variables

Cumming, J.A.; Wooff, D.A.

doi:10.1016/j.csda.2007.02.012

Dimension reduction via principal variables

Cumming, J.A.; Wooff, D.A.

Authors

Dr Jonathan Cumming j.a.cumming@durham.ac.uk
Associate Professor

D.A. Wooff

Abstract

For many large-scale datasets it is necessary to reduce dimensionality to the point where further exploration and analysis can take place. Principal variables are a subset of the original variables and preserve, to some extent, the structure and information carried by the original variables. Dimension reduction using principal variables is considered and a novel algorithm for determining such principal variables is proposed. This method is tested and compared with 11 other variable selection methods from the literature in a simulation study and is shown to be highly effective. Extensions to this procedure are also developed, including a method to determine longitudinal principal variables for repeated measures data, and a technique for incorporating utilities in order to modify the selection process. The method is further illustrated with real datasets, including some larger UK data relating to patient outcome after total knee replacement.

Citation

Cumming, J., & Wooff, D. (2007). Dimension reduction via principal variables. Computational Statistics & Data Analysis, 52(1), 550-565. https://doi.org/10.1016/j.csda.2007.02.012

Journal Article Type	Article
Publication Date	Sep 15, 2007
Deposit Date	Feb 15, 2008
Publicly Available Date	Aug 8, 2016
Journal	Computational Statistics & Data Analysis
Print ISSN	0167-9473
Publisher	Elsevier
Peer Reviewed	Peer Reviewed
Volume	52
Issue	1
Pages	550-565
DOI	https://doi.org/10.1016/j.csda.2007.02.012
Keywords	Variable selection, Principal components, Partial correlation, Partial covariance, Utility.

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Copyright Statement
NOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis, 52, 15 September 2007, 10.1016/j.csda.2007.02.012.