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College of Arts & Sciences
Department of Statistics


Dehan Kong Colloquium

Thursday, February 11, 2016 - 2:45pm

Statistics Department Colloquium

Where: LeConte College, Room 210

Speaker: Dehan Kong

Affiliation: University of North Carolina, Department of Biostatistics

Title:  High-dimensional Matrix Linear Regression Model

Abstract:  We develop a high-dimensional matrix linear regression model
(HMLRM) to correlate matrix responses with high-dimensional scalar
covariates when coefficient matrices have low-rank structures. We
propose a fast and efficient screening procedure based on the spectral
norm to deal with the case that the dimension of scalar covariates is
ultra-high. We develop an efficient estimation procedure based on the
nuclear norm regularization, which explicitly borrows the matrix
structure of coefficient matrices. We systematically investigate various
theoretical properties of our estimators, including estimation
consistency, rank consistency, and the sure independence screening
property under HMLRM. We examine the finite-sample performance of our
methods using simulations and a large-scale imaging genetic dataset
collected by the Alzheimer's Disease Neuroimaging Initiative study.