报告时间：2016年12月31日（周六）上午10:00

报告地点：京师学堂第七会议室

报 告 人：康健，Assistant Professor of Department of Biostatistics, University of Michigan, Ann Arbor Kidney Epidemiology and Cost Center, University of Michigan

报告题目：Scalar-on-Image Regression via the Soft-Thresholded Gaussian Process
报告摘要：The focus of this work is on spatial variable selection for scalar-on-image regression. We propose a new class of Bayesian nonparametric models,  soft-thresholded Gaussian processes and develop the efficient posterior computation algorithms. Theoretically, soft-thresholded Gaussian processes provide large prior support for the spatially varying coefficients that enjoy piecewise smoothness, sparsity and continuity, characterizing the important features of imaging data. Also, under some mild regularity conditions, the soft-thresholded Gaussian process leads to the posterior consistency for both parameter estimation and variable selection for scalar-on-image regression,  even when the number of true predictors is larger than the sample size. The proposed method is illustrated via simulations, compared numerically with existing alternatives and applied to Electroencephalography (EEG) study of alcoholism.