ArticleJournal of the American Statistical Association2015
Bayesian factorizations of big sparse tensors.
Article in Journal of the American Statistical Association, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Bayesian Tensor Decomposition for Clustering Latent Symptom Profiles for Verbal Autopsy Data.Statistics in medicine · 2026Article
- TREE-REGULARIZED BAYESIAN LATENT CLASS ANALYSIS FOR IMPROVING WEAKLY SEPARATED DIETARY PATTERN SUBTYPING IN SMALL-SIZED SUBPOPULATIONS.The annals of applied statistics · 2025Article
- Bayesian Data Sketching for Varying Coefficient Regression Models.Journal of machine learning research : JMLR · 2025Article
- BAYESIAN NESTED LATENT CLASS MODELS FOR CAUSE-OF-DEATH ASSIGNMENT USING VERBAL AUTOPSIES ACROSS MULTIPLE DOMAINS.The annals of applied statistics · 2024Article
- Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data.Journal of machine learning research : JMLR · 2023Article
- Bayesian nonparametric multiway regression for clustered binomial data.Stat (International Statistical Institute) · 2021Article
- PERTURBED FACTOR ANALYSIS: ACCOUNTING FOR GROUP DIFFERENCES IN EXPOSURE PROFILES.The annals of applied statistics · 2021Article
- On the correspondence of deviances and maximum-likelihood and interval estimates from log-linear to logistic regression modelling.Royal Society open science · 2020Article
- High-Dimensional Confounding Adjustment Using Continuous Spike and Slab Priors.Bayesian analysis · 2019Article
- Supervised multiway factorization.Electronic journal of statistics · 2018Article
- Discriminating sample groups with multi-way data.Biostatistics (Oxford, England) · 2017Article
- Tensor Factorization for Precision Medicine in Heart Failure with Preserved Ejection Fraction.Journal of cardiovascular translational research · 2017Review
- Tensor factorization toward precision medicine.Briefings in bioinformatics · 2017Article
- Current approaches used in epidemiologic studies to examine short-term multipollutant air pollution exposures.Annals of epidemiology · 2017Review
- TENSOR DECOMPOSITIONS AND SPARSE LOG-LINEAR MODELS.Annals of statistics · 2017Article
- Exploring dependence between categorical variables: Benefits and limitations of using variable selection within Bayesian clustering in relation to log-linear modelling with interaction terms.Journal of statistical planning and inference · 2016Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
It has become routine to collect data that are structured as multiway arrays (tensors). There is an enormous literature on low rank and sparse matrix factorizations, but limited consideration of extensions to the tensor case in statistics. The most common low rank tensor factorization relies on parallel factor analysis (PARAFAC), which expresses a rank k tensor as a sum of rank one tensors. When observations are only available for a tiny subset of the cells of a big tensor, the low rank assumption is not sufficient and PARAFAC has poor performance. We induce an additional layer of dimension reduction by allowing the effective rank to vary across dimensions of the table. For concreteness, we focus on a contingency table application. Taking a Bayesian approach, we place priors on terms in the factorization and develop an efficient Gibbs sampler for posterior computation. Theory is provided showing posterior concentration rates in high-dimensional settings, and the methods are shown to have excellent performance in simulations and several real data applications.
Indexed as
Identifiers
What OpenQuestion holds
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.