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

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

2007-12-13

arxiv.org/abs/0712.2124v1

Annals of Applied Statistics 2007, Vol. 1, No. 2, 502-537

Statistics

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Published in at http://dx.doi.org/10.1214/07-AOAS126 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/07-AOAS126

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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