Nonparametric Bayes Stochastically Ordered Latent Class Models.

Published

Journal Article

Latent class models (LCMs) are used increasingly for addressing a broad variety of problems, including sparse modeling of multivariate and longitudinal data, model-based clustering, and flexible inferences on predictor effects. Typical frequentist LCMs require estimation of a single finite number of classes, which does not increase with the sample size, and have a well-known sensitivity to parametric assumptions on the distributions within a class. Bayesian nonparametric methods have been developed to allow an infinite number of classes in the general population, with the number represented in a sample increasing with sample size. In this article, we propose a new nonparametric Bayes model that allows predictors to flexibly impact the allocation to latent classes, while limiting sensitivity to parametric assumptions by allowing class-specific distributions to be unknown subject to a stochastic ordering constraint. An efficient MCMC algorithm is developed for posterior computation. The methods are validated using simulation studies and applied to the problem of ranking medical procedures in terms of the distribution of patient morbidity.

Full Text

Duke Authors

Cited Authors

  • Yang, H; O'Brien, S; Dunson, DB

Published Date

  • September 1, 2011

Published In

Volume / Issue

  • 106 / 495

Start / End Page

  • 807 - 817

PubMed ID

  • 22505787

Pubmed Central ID

  • 22505787

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1198/jasa.2011.ap10058

Language

  • eng

Conference Location

  • United States