L2RM: Low-Rank Linear Regression Models for High-Dimensional Matrix Responses

• 成果类型: Article
• 署名作者: Kong, Dehan; An, Baiguo; Zhang, Jingwen; Zhu, Hongtu
• 署名单位: University of Toronto; Capital University of Economics & Business; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2018.1555092
• 发表日期: 2020
• 页码: 403-424
• 关键词: whole-genome association; quantitative trait loci; variable selection; neuroimaging phenotypes; thresholding algorithm; genetic associations; imaging genetics; wide association; Lasso; Consistency

摘要

The aim of this article is to develop a low-rank linear regression model to correlate a high-dimensional response matrix with a high-dimensional vector of covariates when coefficient matrices have low-rank structures. We propose a fast and efficient screening procedure based on the spectral norm of each coefficient matrix to deal with the case when the number of covariates is extremely large. We develop an efficient estimation procedure based on the trace norm regularization, which explicitly imposes the low rank structure of coefficient matrices. When both the dimension of response matrix and that of covariate vector diverge at the exponential order of the sample size, we investigate the sure independence screening property under some mild conditions. We also systematically investigate some theoretical properties of our estimation procedure including estimation consistency, rank consistency, and nonasymptotic error bound under some mild conditions. We further establish a theoretical guarantee for the overall solution of our two-step screening and estimation procedure. We examine the finite-sample performance of our screening and estimation methods using simulations and a large-scale imaging genetic dataset collected by the Philadelphia Neurodevelopmental Cohort study. for this article are available online.