Moment Kernels for Estimating Central Mean Subspace and Central Subspace

• Activity Number: 247 - Sufficient Dimension Reduction and High-Dimensional Data
• Type: Contributed
• Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Weihang Ren* and Xiangrong Yin
• Companies: and University of Kentucky
• Keywords: Central Subspace; Central Mean Subspace; Dimension Reduction; Sufficient Dimension Reduction

Abstract

The T-central subspace, introduced by Luo, Li and Yin (2014), allows one to perform sufficient dimension reduction for any statistical functional of interest. We propose a general estimator using (third) moment kernel to estimate the T-central subspace. In particular, we focus on central mean subspace via the regression mean function, and central subspace via Fourier transform or slicing. Theoretical results are established and simulation studies show the advantages of our proposed methods.