Title: Moment Kernel for Estimating Central Mean Subspace and Central Subspace
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 this talk, we particularly 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.