Multivariate mixed membership modeling: Inferring domain-specific risk profiles
Author:
Affiliation:
1. Harvard-MIT Center for Regulatory Science, Harvard Medical School
2. Emerging Pathogens Institute and Department of Mathematics, University of Florida
3. Department of Statistical Science, Duke University
Publisher
Institute of Mathematical Statistics
Subject
Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability
Reference39 articles.
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2. Erosheva, E. A., Fienberg, S. E. and Joutard, C. (2007). Describing disability through individual-level mixture models for multivariate binary data. Ann. Appl. Stat. 1 502–537.
3. Erosheva, E., Fienberg, S. and Lafferty, J. (2004). Mixed-membership models of scientific publications. Proc. Natl. Acad. Sci. USA 101 5220–5227.
4. Xu, G. (2017). Identifiability of restricted latent class models with binary responses. Ann. Statist. 45 675–707.
5. Stephens, M. (2000). Dealing with label switching in mixture models. J. R. Stat. Soc. Ser. B. Stat. Methodol. 62 795–809.
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