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