Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Publications [#257962] of David B. Dunson


Papers Published

  1. Yang, H; O'Brien, S; Dunson, DB, Nonparametric Bayes Stochastically Ordered Latent Class Models., Journal of the American Statistical Association, vol. 106 no. 495 (September, 2011), pp. 807-817, ISSN 0162-1459 [22505787], [doi]
    (last updated on 2019/05/20)

    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.
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320