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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
Abstract #304236 Presentation 1 Presentation 2
Title: Moment Kernels for Estimating Central Mean Subspace and Central Subspace
Author(s): Weihang Ren* and Xiangrong Yin
Companies: and University of Kentucky
Keywords: Central Subspace; Central Mean Subspace; Dimension Reduction; Sufficient Dimension Reduction

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.

Authors who are presenting talks have a * after their name.

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