Department of Biostatistics Seminar/Workshop Series

Nonparametric Bayes local hierarchical models for biomedical data

David B. Dunson, PhD

Professor, Department of Statistical Science
Duke University, Durham, North Carolina

Wednesday, March 25, 1:30-2:55pm, MRBIII Conference Room 1220

Intended Audience: Persons interested in applied statistics, statistical theory, epidemiology, health services research, clinical trials methodology, statistical computing, statistical graphics, R users or potential users

In modern biomedical research, it is increasingly common to encounter high-dimensional and complex data, such as gene expression profiles over time, longitudinal trajectories in biomarkers and images. Increasing the complexity is the common interest in combining information across data of different sources. Bayesian hierarchical models provide a useful paradigm for addressing these problems, but parametric assumptions and the curse of dimensionality present difficulties. To address these challenges, this talk presents a general class of local partition mixture models, which facilitate sparse modeling of high-dimensional random effects distributions. These models provide a generalization of commonly-used latent class models, finite mixture models and Dirichlet process mixture models, with some clear advantages in terms of favoring a simultaneous reduction in dimensionality and improvement in fit. The methods are illustrated through applications to modeling of reproductive hormone curves, gene expression data and joint modeling of images and captions.

(Presenter Information)