Tuning Variable Selection Procedures by Adding Noise
Tuning Variable Selection Procedures by Adding Noise
AbstractMany variable selection methods for linear regression depend critically on tuning parameters that control the performance of the method, for example, "entry" and "stay" significance levels in forward and backward selection. However, most methods do not adapt the tuning parameters to particular datasets. We propose a general strategy for adapting …