ArticleJournal of the American Statistical Association2018
Fast Moment Estimation for Generalized Latent Dirichlet Models.
Article in Journal of the American Statistical Association, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data.Journal of machine learning research : JMLR · 2023Article
- Empirical Bayes Matrix Factorization.Journal of machine learning research : JMLR · 2021Article
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Authors and funding
4 authors.
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Abstract
We develop a generalized method of moments (GMM) approach for fast parameter estimation in a new class of Dirichlet latent variable models with mixed data types. Parameter estimation via GMM has computational and statistical advantages over alternative methods, such as expectation maximization, variational inference, and Markov chain Monte Carlo. A key computational advantage of our method, Moment Estimation for latent Dirichlet models (MELD), is that parameter estimation does not require instantiation of the latent variables. Moreover, performance is agnostic to distributional assumptions of the observations. We derive population moment conditions after marginalizing out the sample-specific Dirichlet latent variables. The moment conditions only depend on component mean parameters. We illustrate the utility of our approach on simulated data, comparing results from MELD to alternative methods, and we show the promise of our approach through the application to several datasets. Supplementary materials for this article are available online.
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