ArticleJournal of the American Statistical Association2012
Simplex Factor Models for Multivariate Unordered Categorical Data.
Article in Journal of the American Statistical Association, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 59 citations in OpenAlex.
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- Fast Moment Estimation for Generalized Latent Dirichlet Models.Journal of the American Statistical Association · 2018Article
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- 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
- Bayesian Conditional Tensor Factorizations for High-Dimensional Classification.Journal of the American Statistical Association · 2016Article
- Bayesian factorizations of big sparse tensors.Journal of the American Statistical Association · 2015Article
- Longitudinal Mixed Membership Trajectory Models for Disability Survey Data.The annals of applied statistics · 2014Article
- BAYESIAN SEMIPARAMETRIC ANALYSIS FOR TWO-PHASE STUDIES OF GENE-ENVIRONMENT INTERACTION.The annals of applied statistics · 2013Article
- Bayesian modeling of temporal dependence in large sparse contingency tables.Journal of the American Statistical Association · 2013Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Gaussian latent factor models are routinely used for modeling of dependence in continuous, binary, and ordered categorical data. For unordered categorical variables, Gaussian latent factor models lead to challenging computation and complex modeling structures. As an alternative, we propose a novel class of simplex factor models. In the single-factor case, the model treats the different categorical outcomes as independent with unknown marginals. The model can characterize flexible dependence structures parsimoniously with few factors, and as factors are added, any multivariate categorical data distribution can be accurately approximated. Using a Bayesian approach for computation and inferences, a Markov chain Monte Carlo (MCMC) algorithm is proposed that scales well with increasing dimension, with the number of factors treated as unknown. We develop an efficient proposal for updating the base probability vector in hierarchical Dirichlet models. Theoretical properties are described, and we evaluate the approach through simulation examples. Applications are described for modeling dependence in nucleotide sequences and prediction from high-dimensional categorical features.
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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.