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

Math @ Duke



Publications [#257845] of David B. Dunson


Papers Published

  1. Wade, S; Dunson, DB; Petrone, S; Trippa, L, Improving prediction from dirichlet process mixtures via enrichment, Journal of machine learning research : JMLR, vol. 15 (January, 2014), pp. 1041-1071, ISSN 1532-4435
    (last updated on 2018/02/17)

    Flexible covariate-dependent density estimation can be achieved by modelling the joint density of the response and covariates as a Dirichlet process mixture. An appealing aspect of this approach is that computations are relatively easy. In this paper, we examine the predictive performance of these models with an increasing number of covariates. Even for a moderate number of covariates, we find that the likelihood for x tends to dominate the posterior of the latent random partition, degrading the predictive performance of the model. To overcome this, we suggest using a different nonparametric prior, namely an enriched Dirichlet process. Our proposal maintains a simple allocation rule, so that computations remain relatively simple. Advantages are shown through both predictive equations and examples, including an application to diagnosis Alzheimer's disease. © 2014 Sara Wade, David B. Dunson, Sonia Petrone and Lorenzo Trippa.
ph: 919.660.2800
fax: 919.660.2821

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