We consider the problem of selecting grouped variables (factors) for accurate prediction in regression. Such a problem arises naturally in many practical situations with the multi-factor ...

We introduce a fast stepwise regression method, called the orthogonal greedy algorithm (OGA), that selects input variables to enter a p-dimensional linear regression model (with p ≫ n, the sample size ...

stepwise regression, forward and backward. This method is a modification of the forward-selection method in that variables already in the model do not necessarily stay there.

Comparison of stepwise, backward and forward selection and the Hierarchically Well-Formulated Rule (HWFR) in logistic regressions.