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Activity Number: 186 - Contributed Poster Presentations: International Chinese Statistical Association
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: International Chinese Statistical Association
Abstract #304182
Title: Sufficient Dimension Reduction via Fourier Transformation
Author(s): Pei Wang* and Xiangrong Yin
Companies: University of Kentucky and University of Kentucky
Keywords: Sufficient Dimension Reduction; Fourier Transformation; Variable Selection

Sufficient dimension reduction (SDR), replacing the original predictors with a few linear combinations of them while keeping all the regression information, has been useful and popular in the past thirty years or so. In this project, we proposed a new SDR through Fourier Transformation. Our method is suitable for both univariate and multivariate responses. We provide an estimation method to determine the reduced dimension and develop a variable selection procedure. Theoretical results are established. The efficacy of our method is demonstrated by simulations and a real data example.

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

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