ArticleAnnals of statistics2017
TENSOR DECOMPOSITIONS AND SPARSE LOG-LINEAR MODELS.
Article in Annals of statistics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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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
8 citing papers in PubMed.
- Bayesian Tensor Decomposition for Clustering Latent Symptom Profiles for Verbal Autopsy Data.Statistics in medicine · 2026Article
- 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
- Integrating sample similarities into latent class analysis: a tree-structured shrinkage approach.Biometrics · 2023Article
- Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data.Journal of machine learning research : JMLR · 2023Article
- COMPOSITE MIXTURE OF LOG-LINEAR MODELS WITH APPLICATION TO PSYCHIATRIC STUDIES.The annals of applied statistics · 2022Article
- Bayesian nonparametric multiway regression for clustered binomial data.Stat (International Statistical Institute) · 2021Article
- Theoretical limits of microclustering for record linkage.Biometrika · 2018Article
- 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
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. We derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.
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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.