ArticleStatistics in biosciences2018
Tucker Tensor Regression and Neuroimaging Analysis.
Article in Statistics in biosciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Regularized Tensor Quantile Regression With Applications to Neuroimaging Data Analysis.Statistics in medicine · 2026Article
- Bayesian thresholded modeling for integrating brain node and network predictors.Biostatistics (Oxford, England) · 2025Article
- Efficient Decision Trees for Tensor Regressions.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Bayesian scalar-on-network regression with applications to brain functional connectivity.Biometrics · 2025Article
- Partial Quantile Tensor Regression.Journal of the American Statistical Association · 2024Article
- TENSOR REGRESSION FOR INCOMPLETE OBSERVATIONS WITH APPLICATION TO LONGITUDINAL STUDIES.The annals of applied statistics · 2024Article
- TENSOR QUANTILE REGRESSION WITH LOW-RANK TENSOR TRAIN ESTIMATION.The annals of applied statistics · 2024Article
- A tensor based varying-coefficient model for multi-modal neuroimaging data analysis.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2024Article
- Bayesian tensor-on-tensor regression with efficient computation.Statistics and its interface · 2024Article
- A canonical polyadic tensor basis for fast Bayesian estimation of multi-subject brain activation patterns.Frontiers in neuroinformatics · 2024Article
- Heterogeneity Analysis on Multi-state Brain Functional Connectivity and Adolescent Neurocognition.Journal of the American Statistical Association · 2024Article
- Bayesian predictive modeling of multi-source multi-way data.Computational statistics & data analysis · 2023Article
- Tensor response quantile regression with neuroimaging data.Biometrics · 2023Article
- LOCUS: A REGULARIZED BLIND SOURCE SEPARATION METHOD WITH LOW-RANK STRUCTURE FOR INVESTIGATING BRAIN CONNECTIVITY.The annals of applied statistics · 2023Article
- Aberrant Structure MRI in Parkinson's Disease and Comorbidity with Depression Based on Multinomial Tensor Regression Analysis.Journal of personalized medicine · 2022Article
- TENSOR QUANTILE REGRESSION WITH APPLICATION TO ASSOCIATION BETWEEN NEUROIMAGES AND HUMAN INTELLIGENCE.The annals of applied statistics · 2021Article
- Localization and Diagnosis of Attention-Deficit/Hyperactivity Disorder.Healthcare (Basel, Switzerland) · 2021Article
- Generalized Co-Clustering Analysis via Regularized Alternating Least Squares.Computational statistics & data analysis · 2020Article
- Sparse and Low-rank Tensor Estimation via Cubic Sketchings.IEEE transactions on information theory · 2020Article
- TENSOR GENERALIZED ESTIMATING EQUATIONS FOR LONGITUDINAL IMAGING ANALYSIS.Statistica Sinica · 2019Article
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4 authors.
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Abstract
Neuroimaging data often take the form of high dimensional arrays, also known as tensors. Addressing scientific questions arising from such data demands new regression models that take multidimensional arrays as covariates. Simply turning an image array into a vector would both cause extremely high dimensionality and destroy the inherent spatial structure of the array. In a recent work, Zhou et al. (2013) proposed a family of generalized linear tensor regression models based upon the CP (CANDECOMP/PARAFAC) decomposition of regression coefficient array. Low rank approximation brings the ultrahigh dimensionality to a manageable level and leads to efficient estimation. In this article, we propose a tensor regression model based on the more flexible Tucker decomposition. Compared to the CP model, Tucker regression model allows different number of factors along each mode. Such flexibility leads to several advantages that are particularly suited to neuroimaging analysis, including further reduction of the number of free parameters, accommodation of images with skewed dimensions, explicit modeling of interactions, and a principled way of image downsizing. We also compare the Tucker model with CP numerically on both simulated data and a real magnetic resonance imaging data, and demonstrate its effectiveness in finite sample performance.
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