Title: Structure identification and sparse learning for image-on-scalar regression with application to imaging genetics studies
Authors: Xinyi Li - University of North Carolina at Chapel Hill (United States) [presenting]
Lily Wang - Iowa State University (United States)
Huixia Judy Wang - George Washington University (United States)
Abstract: High-dimensional image-on-scalar regression is considered, 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 responses 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. We then further develop a unified approach for simultaneous sparse learning and model structure identification in the presence of ultra-high-dimensional covariates. Our method can identify zero, nonzero constant and spatially varying components correctly and efficiently. The estimators of constant coefficients and varying coefficient functions are consistent and asymptotically normal for constant coefficient estimators. The method is evaluated by Monte Carlo simulation studies and applied to a dataset provided by the Alzheimers Disease Neuroimaging Initiative.