Cumming, J. A. and Wooff, D. A. (2007) 'Dimension reduction via principal variables.', Computational statistics & data analysis., 52 (1). pp. 550-565.
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.
|Keywords:||Variable selection, Principal components, Partial correlation, Partial covariance, Utility.|
|Full text:||Full text not available from this repository.|
|Publisher Web site:||http://dx.doi.org/10.1016/j.csda.2007.02.012|
|Record Created:||15 Feb 2008|
|Last Modified:||27 Apr 2009 09:34|
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