DESCRIBING DISABILITY THROUGH INDIVIDUAL-LEVEL MIXTURE MODELS FOR MULTIVARIATE BINARY DATA.

By Elena A. Erosheva, Stephen E. Fienberg, Cyrille Joutard. 2007

151 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Demography, Health

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. David M. Blei
2. Jon D. McAuliffe
3. Alp Kucukelbir
4. Jordan L. Boyd-Graber
5. Chong Wang
6. Eric P. Xing
7. Chengxiang Zhai
8. Stephan Mandt

In Demography

1. Daniel Manrique-Vallier
2. John D. Lafferty
3. David M. Blei
4. Jerome P. Reiter

In Health

1. Daniel Manrique-Vallier
2. John D. Lafferty
3. David M. Blei
4. Jerome P. Reiter
5. Josep Domingo-Ferrer
6. Kristian Lum
7. James E. Johndrow

Citations in the corpus

• Variational Inference: A Review for Statisticians (2016, background): “…McAuliffe, 2010), optimal control and reinforcement learning (Van Den Broek et al., 2008; Furmston and Barber, 2010), statistical network analysis (Wiggins and Hofman, 2008; Airoldi et al., 2008), astrophysics (Regier et al., 2015), and the social sciences (Erosheva et al., 2007; Grimmer, 2011).”
• Bayesian Estimation of Discrete Multivariate Latent Structure Models With Structural Zeros (2014, methodology): “Examples of these algorithms include Patz and Junker (1999), Ishwaran and James (2001), and Erosheva, Fienberg, and Joutard (2007), among others.”
• Syntactic Topic Models (2008, background): “Rather than treating words as the exchangeable unit within a document, the words of the sentences must conform to the structure of a parse tree.”
• A correlated topic model of Science (2007, methodology): “Similar models were independently developed for disability survey data [10, 11] and population genetics [22].”