Sufficient dimension reduction based on distance‐weighted discrimination

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 dimension p of 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 fixed p . Finally, we demonstrate the advantages of our algorithm using simulated and real datasets.