Clemson University

Sparse Learning of Imaging Genetics Studies in Alzheimer’s Disease -- Xinyi Li

This article considers high-dimensional image-on-scalar regression, where the spatial heterogeneity of covariate effects on imaging responses is investigated via a flexible partially linear spatially varying coefficient model.

 

To tackle the challenges of spatial smoothing over the imaging response’s complex domain consisting of regions of interest, we approximate the spatially varying coefficient functions via bivariate spline functions over triangulation. We first study estimation when the active constant coefficients and varying coefficient functions are known in advance. 

Friday, February 25 at 2:30pm to 3:30pm

McAdams Hall, 114
821 McMillan Rd., Clemson, SC 29634, USA

Notice of Non-Discrimination

Target Audience

All Audiences

Departments

College of Engineering, Computing and Applied Sciences, School of Computing

Contact Name:

Kai Liu

Contact Phone:

7203662691

Contact Email:

kail@clemson.edu

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