ArticleJournal of the American Statistical Association2020
L2RM: Low-rank Linear Regression Models for High-dimensional Matrix Responses.
Article in Journal of the American Statistical Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Bayesian Longitudinal Network Regression With Application to Brain Connectome Genetics.Statistics in medicine · 2025Article
- Identifying genetic variants for brain connectivity using Ball Covariance Ranking and Aggregation.Journal of the American Statistical Association · 2025Article
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- Wasserstein F-tests for Fréchet regression on Bures-Wasserstein manifolds.Journal of machine learning research : JMLR · 2025Article
- Bayesian mixed model inference for genetic association under related samples with brain network phenotype.Biostatistics (Oxford, England) · 2024Article
- A multivariate to multivariate approach for voxel-wise genome-wide association analysis.Statistics in medicine · 2024Article
- EEG functional connectivity as a Riemannian mediator: An application to malnutrition and cognition.Human brain mapping · 2024Article
- Imaging mediation analysis for longitudinal outcomes: a case study of childhood brain tumor survivorship.Statistics and its interface · 2024Article
- Low-rank regression models for multiple binary responses and their applications to cancer cell-line encyclopedia data.Journal of the American Statistical Association · 2024Article
- Generalized Connectivity Matrix Response Regression with Applications in Brain Connectivity Studies.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Integrative Bayesian tensor regression for imaging genetics applications.Frontiers in neuroscience · 2023Article
- Genetic underpinnings of brain structural connectome for young adults.Journal of the American Statistical Association · 2023Article
- Functional hybrid factor regression model for handling heterogeneity in imaging studies.Biometrika · 2022Article
- Bayesian network mediation analysis with application to the brain functional connectome.Statistics in medicine · 2022Article
- Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer's Disease.Journal of the American Statistical Association · 2022Article
- Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification.IEEE transactions on medical imaging · 2021Article
- Brain functional connectivity analysis based on multi-graph fusion.Medical image analysis · 2021Article
- Super-variants identification for brain connectivity.Human brain mapping · 2021Article
- Matrix Linear Discriminant Analysis.Technometrics : a journal of statistics for the physical, chemical, and engineering sciences · 2020Article
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4 authors.
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Abstract
The aim of this paper is to develop a low-rank linear regression model (L2RM) 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 in order 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 non-asymptotic 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 (PNC) study.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.