Stats4Grads: Dimension Reduction via Principal Variables
21 February 2007 13:00 in CM105
For many large-scale data sets 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 stepwise algorithm for determining such principal variables is proposed. This method is tested and compared with eleven other variable selection methods from the literature in a simulation study and is shown to be highly effective. Some 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 data sets, including some larger UK data relating to patient outcome after total knee replacement.
Contact thomai.tsiftsi(@)durham.ac.uk for more information
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