David B. Dunson

Image of David B. Dunson

Arts and Sciences Professor of Statistical Science

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.

Appointments and Affiliations
  • Arts and Sciences Professor of Statistical Science
  • Professor of Statistical Science
  • Professor in the Department of Electrical and Computer Engineering
Contact Information:
Education:

  • Ph.D. Emory University, 1997
  • B.S. Pennsylvania State University, 1994

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: Bayesian Inference and Modern Statistical Methods
  • STA 393: Research Independent Study
  • STA 601: Bayesian and Modern Statistical Data Analysis
  • STA 993: Independent Study
  • STA 994: Independent Study

Representative Publications: (More Publications)
    • Kessler, DC; Hoff, PD; Dunson, DB, Marginally specified priors for non-parametric Bayesian estimation, Journal of the Royal Statistical Society Series B: Statistical Methodology (2014), pp. n/a-n/a [10.1111/rssb.12059] [abs].
    • Chen, CWS; Dunson, D; Frühwirth-Schnatter, S; Walker, SG, Special issue on Bayesian computing, methods and applications, Computational Statistics and Data Analysis, vol 71 (2014) [10.1016/j.csda.2013.10.011] [abs].
    • Pati, D; Dunson, DB, Bayesian nonparametric regression with varying residual density, Annals of the Institute of Statistical Mathematics, vol 66 no. 1 (2014), pp. 1-31 [abs].
    • Xing, Z; Nicholson, B; Jimenez, M; Veldman, T; Hudson, L; Lucas, J; Dunson, D; Zaas, AK; Woods, CW; Ginsburg, GS; Carin, L, Bayesian modeling of temporal properties of infectious disease in a college student population, Journal of Applied Statistics, vol 41 no. 6 (2014), pp. 1358-1382 [abs].
    • Wade, S; Dunson, DB; Petrone, S; Trippa, L, Improving prediction from dirichlet process mixtures via enrichment, Journal of Machine Learning Research, vol 15 (2014), pp. 1041-1071 [abs].