ArticleJournal of the American Statistical Association2011
Population Value Decomposition, a Framework for the Analysis of Image Populations.
Article in Journal of the American Statistical Association, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed.
- A tensor-based population value decomposition to explain rectal toxicity after prostate cancer radiotherapy.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2013Trial
- Local transcriptional covariation produces accurate estimates of cell phenotype.Bioinformatics (Oxford, England) · 2026Article
- Regularized Tensor Quantile Regression With Applications to Neuroimaging Data Analysis.Statistics in medicine · 2026Article
- Mediation with External Summary Statistic Information.Biostatistics (Oxford, England) · 2025Article
- Bayesian estimation of covariate assisted principal regression for brain functional connectivity.Biostatistics (Oxford, England) · 2025Article
- Article
- Statistical Learning Methods for Neuroimaging Data Analysis with Applications.Annual review of biomedical data science · 2023Review
- Two-stage linked component analysis for joint decomposition of multiple biologically related data sets.Biostatistics (Oxford, England) · 2022Article
- Low-Rank Tucker-2 Model for Multi-Subject fMRI Data Decomposition With Spatial Sparsity Constraint.IEEE transactions on medical imaging · 2022Article
- Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace.Journal of machine learning research : JMLR · 2021Article
- Urinary levels of environmental phenols and parabens and antioxidant enzyme activity in the blood of women.Environmental research · 2020Article
- Multiple Brain Networks Mediating Stimulus-Pain Relationships in Humans.Cerebral cortex (New York, N.Y. : 1991) · 2020Article
- The Roles of Statistics in Human Neuroscience.Brain sciences · 2019Article
- Extraction of Time-Varying Spatiotemporal Networks Using Parameter-Tuned Constrained IVA.IEEE transactions on medical imaging · 2019Article
- Exposure to bisphenol A, chlorophenols, benzophenones, and parabens in relation to reproductive hormones in healthy women: A chemical mixture approach.Environment international · 2018Article
- High-dimensional multivariate mediation with application to neuroimaging data.Biostatistics (Oxford, England) · 2018Article
- 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
- A hierarchical modeling approach to estimate regional acute health effects of particulate matter sources.Statistics in medicine · 2017Article
- Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data.Biostatistics (Oxford, England) · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Images, often stored in multidimensional arrays, are fast becoming ubiquitous in medical and public health research. Analyzing populations of images is a statistical problem that raises a host of daunting challenges. The most significant challenge is the massive size of the datasets incorporating images recorded for hundreds or thousands of subjects at multiple visits. We introduce the population value decomposition (PVD), a general method for simultaneous dimensionality reduction of large populations of massive images. We show how PVD can be seamlessly incorporated into statistical modeling, leading to a new, transparent, and rapid inferential framework. Our PVD methodology was motivated by and applied to the Sleep Heart Health Study, the largest community-based cohort study of sleep containing more than 85 billion observations on thousands of subjects at two visits. This article has supplementary material online.
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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.