David B. Dunson

Image of David B. Dunson

Professor (primary appt: Statistical Science)

Contact Information:
Education:

PhDEmory University1997

Research Interests:

Development of Bayesian methods motivated by applications with complex and high-dimensional data. A particular focus is on nonparametric Bayes approaches for conditional distributions and for flexible borrowing of information. I am also interested in methods for accommodating model uncertainty in hierarchical models, and in latent variable methods, including structural equation models. A recent interest has been in functional data analysis.

Specialties:

Bayesian Statistics
Complex Hierarchical and Latent Variable Modelling
Nonparametric Statistical Modelling
Model Selection
Statistical Modeling

Courses Taught:
  • STA 360.001 - BAYESIAN AND MODERN STATISTICS
  • STA 360.01L - BAYESIAN AND MODERN STATISTICS
  • STA 601.01 - BAYESIAN AND MODERN STATISTICS
  • STA 601.01L - BAYESIAN AND MODERN STATISTICS
  • STA 601.02L - BAYESIAN AND MODERN STATISTICS
  • STA 360.001 - BAYESIAN AND MODERN STATISTICS
  • STA 360.01L - BAYESIAN AND MODERN STATISTICS
  • STA 601.01 - BAYESIAN AND MODERN STATISTICS
  • STA 601.01L - BAYESIAN AND MODERN STATISTICS
  • STA 601.02L - BAYESIAN AND MODERN STATISTICS

Representative Publications: (More Publications)
    • D.B. Dunson, Nonparametric Bayes local partition models for random effects, Biometrika, vol 96 (2009), pp. 249-262.
    • J.L. Bigelow and D.B. Dunson, Bayesian semiparametric joint models for functional predictors, Journal of the American Statistical Association, vol 104 (2009), pp. 26-36.
    • D.B. Dunson and Chuanhua Xing, Nonparametric Bayes modeling of multivariate categorical data, Journal of the American Statistical Association, vol 104 (2009), pp. 1042-1051.
    • D.B. Dunson and J-H. Park, Kernel stick-breaking processes, Biometrika, vol 95 (2008), pp. 307-323.
    • D.B. Dunson, A.H. Herring and S.M. Engel, Bayesian selection and clustering of polymorphisms in functionally-related genes, Journal of the American Statistical Association, vol 103 (2008), pp. 534-546.