Sufficient dimension reduction based on distance-weighted discrimination

摘要

In this paper, we introduce a sufficient dimension reduction (SDR) algorithm based on distance-weighted discrimination (DWD). Our methods is shown to be robust on the dimensionpof the predictors in our problem, and it also utilizes some new computational results in the DWD literature to propose a computationally faster algorithm than previous classification-based algorithms in the SDR literature. In addition to the theoretical results of similar methods we prove the consistency of our estimate for divergent number ofp. Finally, we demonstrate the advantages of our algorithm using simulated and real datasets.