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Activity Number: 594 - Methods for Analysis of High-Dimensional Data
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #327145 Presentation
Title: Optimal Quadratic Estimators Using Fourier Transform in the Central Subspaces
Author(s): Jiaying Weng* and Xiangrong Yin
Companies: University of Kentucky and University of Kentucky
Keywords: Sufficient Dimension Reduction; Fourier Transform; Central subspace; Variable selection; Predictors hypothesis tests

We develop an optimal inverse regression estimator using Fourier transforms, Fourier transform inverse regression estimator (FT-IRE). Most inverse dimension reduction techniques require the number of slices, which could be an issue. Using Fourier transform avoid such an issue. The degenerate and robust Fourier transform inverse regression estimators are also introduced for less computational and robust cases. Along the line, the group LASSO is used for variable selection. Asymptotic properties and the marginal dimension and predictors hypothesis tests are studied. Various simulations studies and a real data analysis of Australian Institute of Sport are used to demonstrate the advantages of our proposed methods.

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

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