B0459
Title: Sufficient dimension reduction and variable selection by feature filter
Authors: Pei Wang - Bowling Green State University (United States) [presenting]
Abstract: Sufficient dimension reduction, replacing the original predictors with a few linear combinations while keeping all the regression information, has been widely used in the past thirty years or so. We propose a new sufficient dimension reduction method, with two estimation procedures, for estimating central mean subspace through a novel approach of feature filter. The method is suitable for both univariate and multivariate responses. Asymptotic results are established. Furthermore, we provide estimation methods to determine the structural dimension, obtain a sparse estimator and deal with large $p$ small $n$ data. Simulations and a real data example demonstrate the efficacy of our method.
