Describing disability through individual-level mixture models for multivariate binary data

DOI10.1214/07-AOAS126zbMath1126.62101arXiv0712.2124OpenAlexW2153305362WikidataQ33936986 ScholiaQ33936986MaRDI QIDQ2466476

Cyrille Joutard, Stephen E. Fienberg, Elena A. Erosheva

Publication date: 15 January 2008

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0712.2124

zbMATH Keywords

tables Bayesian estimation activities of daily living variational approximation latent class grade of membership functional disability mixed membership partial membership

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics to social sciences (62P25) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40) Estimation in survival analysis and censored data (62N02)

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