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Activity Number: 516 - Case Studies of Scalar-On-Image Regression
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Imaging
Abstract #302883
Title: Sparse Groupwise Envelope Model for Response Variable Selection in Imaging Genetic Analysis
Author(s): Yeonhee Park* and Zhihua Su and Hongtu Zhu
Companies: Medical University of South Carolina and University of Florida and DiDi Chuxing and UNC-Chapel Hill
Keywords: Dimension reduction; Envelope model; Grassmann manifold; Variable selection
Abstract:

This paper aims to develop a sparse groupwise envelope model for response variable selection and efficient estimation under multivariate linear regression. When the observations belong to several groups, the sparse groupwise envelope model can accommodate distinct regression coefficients and heteroscedastic error structures for different groups. Consistency and the oracle property of the proposed method is established. Simulation studies and the analysis of two data sets collected by the Alzheimer's Disease study and sourced from the Philadelphia Neurodevelopmental Cohort (PNC) show the effectiveness of our efficient estimation and response variable selection. Data for the Alzheimer's Disease study used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.


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