A stepwise regression algorithm for high-dimensional variable selection

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 [GRAPHICS] -value among all the candidates and is smaller than a predetermined threshold. Instead of using conventional information types of criteria, the threshold is determined by a lower percentile of the beta distribution. We conducted extensive simulation studies to evaluate the performance of the proposed algorithm for various statistical models and found it to be very competitive and robust compared with several popular high-dimensional variable selection methods.