A stepwise regression algorithm for high-dimensional variable selection

摘要

We propose a new stepwise regression algorithm with a simple stopping rule for the identification of influential predictors and interactions among a huge number of variables in various statistical models. Like conventional stepwise regression, at each forward selection step, a variable is included in the current model if the test statistic of the enlarged model with the predictor against the current model has the minimum