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December 2007 Describing disability through individual-level mixture models for multivariate binary data
Elena A. Erosheva, Stephen E. Fienberg, Cyrille Joutard
Ann. Appl. Stat. 1(2): 502-537 (December 2007). DOI: 10.1214/07-AOAS126

Abstract

Data on functional disability are of widespread policy interest in the United States, especially with respect to planning for Medicare and Social Security for a growing population of elderly adults. We consider an extract of functional disability data from the National Long Term Care Survey (NLTCS) and attempt to develop disability profiles using variations of the Grade of Membership (GoM) model. We first describe GoM as an individual-level mixture model that allows individuals to have partial membership in several mixture components simultaneously. We then prove the equivalence between individual-level and population-level mixture models, and use this property to develop a Markov Chain Monte Carlo algorithm for Bayesian estimation of the model. We use our approach to analyze functional disability data from the NLTCS.

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Elena A. Erosheva. Stephen E. Fienberg. Cyrille Joutard. "Describing disability through individual-level mixture models for multivariate binary data." Ann. Appl. Stat. 1 (2) 502 - 537, December 2007. https://doi.org/10.1214/07-AOAS126

Information

Published: December 2007
First available in Project Euclid: 30 November 2007

zbMATH: 1126.62101
MathSciNet: MR2415745
Digital Object Identifier: 10.1214/07-AOAS126

Keywords: Activities of daily living , Bayesian estimation , functional disability , grade of membership , latent class , Mixed membership , partial membership , variational approximation

Rights: Copyright © 2007 Institute of Mathematical Statistics

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Vol.1 • No. 2 • December 2007
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