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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Two new approaches to variable selection in regression are presented. The key idea in both approaches is to calibrate an existing tunable selection method in order to achieve desirable properties of selected models. Attention is restricted to forward selection for which the tuning parameter is the familiar alpha-to-enter. In the first approach, Noise Added Model Selection (NAMS), parametric bootstrap-like data sets are generated by incrementally adding noise to the response variable, and alpha is tuned by tracking the effect of added noise on selected models' mean squared errors for different alpha values. In the second approach, Variable Added Model Selection (VAMS), random phony predictor variables are added to the data set, and alpha is tuned by tracking the proportion of falsely included phony variables in the models selected for different alpha values.
