ArticleJournal of the American Statistical Association2016
Bayesian Conditional Tensor Factorizations for High-Dimensional Classification.
Article in Journal of the American Statistical Association, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed.
- Early detection of COPD patients' symptoms with personal environmental sensors: a remote sensing framework using probabilistic latent component analysis with linear dynamic systems.Neural computing & applications · 2023Article
- Determining the Number of Latent Factors in Statistical Multi-Relational Learning.Journal of machine learning research : JMLR · 2019Article
- Bayesian Nonparametric Modeling of Categorical Data for Information Fusion and Causal Inference.Entropy (Basel, Switzerland) · 2018Article
- 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
- Bayesian factorizations of big sparse tensors.Journal of the American Statistical Association · 2015Article
- Learning phenotype densities conditional on many interacting predictors.Bioinformatics (Oxford, England) · 2014Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
In many application areas, data are collected on a categorical response and high-dimensional categorical predictors, with the goals being to build a parsimonious model for classification while doing inferences on the important predictors. In settings such as genomics, there can be complex interactions among the predictors. By using a carefully-structured Tucker factorization, we define a model that can characterize any conditional probability, while facilitating variable selection and modeling of higher-order interactions. Following a Bayesian approach, we propose a Markov chain Monte Carlo algorithm for posterior computation accommodating uncertainty in the predictors to be included. Under near low rank assumptions, the posterior distribution for the conditional probability is shown to achieve close to the parametric rate of contraction even in ultra high-dimensional settings. The methods are illustrated using simulation examples and biomedical applications.
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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.