Nonparametric Bayes Stochastically Ordered Latent Class Models

• Activity Number: 64
• Type: Topic Contributed
• Date/Time: Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Hongxia Yang*+ and David Dunson
• Companies: Duke University and Duke University
• Keywords: Factor analysis ; Latent variables ; Model-based clustering ; Order restriction ; Random probability measure ; Stick-breaking

Abstract

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. However, Bayes methods relying on Markov chain Monte Carlo sampling encounter a challenging label ambiguity problem, which makes it difficult to perform inferences on class-specific quantities. 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 and label switching problems by allowing class-specific distributions to be unknown subject to a stochastic ordering constraint. The methods are validated using simulation studies and applied to the problem of ranking medical procedures in terms of the distribution of patient morbidity.