Dirichlet Process Mixture Models for Modeling and Generating Synthetic Versions of Nested Categorical Data

@article{Hu2014DirichletPM,
  title={Dirichlet Process Mixture Models for Modeling and Generating Synthetic Versions of Nested Categorical Data},
  author={Jingchen Hu and Jerome P. Reiter and Quanli Wang},
  journal={arXiv: Methodology},
  year={2014}
}
We present a Bayesian model for estimating the joint distribution of multivariate categorical data when units are nested within groups. Such data arise frequently in social science settings, for example, people living in households. The model assumes that (i) each group is a member of a group-level latent class, and (ii) each unit is a member of a unit-level latent class nested within its group-level latent class. This structure allows the model to capture dependence among units in the same… 

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