ArticleJournal of the American Statistical Association2023
Partially Observed Dynamic Tensor Response Regression.
Article in Journal of the American Statistical Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- MULTI-OBJECT DATA INTEGRATION IN THE STUDY OF PRIMARY PROGRESSIVE APHASIA.The annals of applied statistics · 2025Article
- STATISTICAL INFERENCE FOR MEAN FUNCTIONS OF COMPLEX 3D OBJECTS.Statistica Sinica · 2025Article
- Partial Quantile Tensor Regression.Journal of the American Statistical Association · 2024Article
- Tensor response quantile regression with neuroimaging data.Biometrics · 2023Article
- 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
- Partially Observed Dynamic Tensor Response Regression.Journal of the American Statistical Association · 2023Article
- Envelope method with ignorable missing data.Electronic journal of statistics · 2021Article
- Sparse and Low-rank Tensor Estimation via Cubic Sketchings.IEEE transactions on information theory · 2020Article
- L2RM: Low-rank Linear Regression Models for High-dimensional Matrix Responses.Journal of the American Statistical Association · 2020Article
- A Bayesian approach to joint modeling of matrix-valued imaging data and treatment outcome with applications to depression studies.Biometrics · 2020Article
- Tensor canonical correlation analysis.Stat · 2020Article
- Tensor-on-tensor regression.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2018Article
- Supervised multiway factorization.Electronic journal of statistics · 2018Article
- Groupwise envelope models for imaging genetic analysis.Biometrics · 2017Article
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Authors and funding
4 authors.
Funding
Abstract
In modern data science, dynamic tensor data prevail in numerous applications. An important task is to characterize the relationship between dynamic tensor datasets and external covariates. However, the tensor data are often only partially observed, rendering many existing methods inapplicable. In this article, we develop a regression model with a partially observed dynamic tensor as the response and external covariates as the predictor. We introduce the low-rankness, sparsity, and fusion structures on the regression coefficient tensor, and consider a loss function projected over the observed entries. We develop an efficient nonconvex alternating updating algorithm, and derive the finite-sample error bound of the actual estimator from each step of our optimization algorithm. Unobserved entries in the tensor response have imposed serious challenges. As a result, our proposal differs considerably in terms of estimation algorithm, regularity conditions, as well as theoretical properties, compared to the existing tensor completion or tensor response regression solutions. We illustrate the efficacy of our proposed method using simulations and two real applications, including a neuroimaging dementia study and a digital advertising study.
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Registered trials
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.