Describing Disability through Individual-level Mixture Models for Multivariate Binary Data.

@article{Erosheva2007DescribingDT,
  title={Describing Disability through Individual-level Mixture Models for Multivariate Binary Data.},
  author={Elena A. Erosheva and Stephen E. Fienberg and Cyrille Joutard},
  journal={The annals of applied statistics},
  year={2007},
  volume={1 2},
  pages={346-384}
}
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… CONTINUE READING
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