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Variable Selection Methods for Giga-Bases - INFORMS NY

Variable Selection Methods for Giga-Bases - INFORMS NY

Stepwise variable

Stepwise variable selection (cont. 2). Drawbacks. •Can miss on important variables that have combined effect on dependent variable, but may be no or small individual effect. •Inference with many independent variables may be invalid (most likely). •Sample variations imply that different variables be selected, due to similar semi- or partial correlations. •Based on idea that correlated variables contain redundant information, which is not necessarily true. 9/18/03 18

Backward variable selection. •Start with all the variables of the set, and eliminate one at a time based on a threshold of goodness-of-fit given by the lowest (partial) F-value. •Re-compute at each stage, and stop when lowest partial is higher than the threshold F-value. Drawback. Once a variable is removed, it cannot be brought back in all alternative models with variables already removed are missing. 9/18/03 19

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