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
  • Faculty Network Member of the Duke Institute for Brain Sciences
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 790: Special Topics in Statistics
  • STA 993: Independent Study
  • STA 994: Independent Study

Representative Publications: (More Publications)
    • Durante, D; Dunson, DB, Bayesian dynamic financial networks with time-varying predictors, Statistics and Probability Letters, vol 93 (2014), pp. 19-26 [10.1016/j.spl.2014.06.015] [abs].
    • Lin, L; Dunson, DB, Bayesian monotone regression using Gaussian process projection, Biometrika, vol 101 no. 2 (2014), pp. 303-317 [10.1093/biomet/ast063] [abs].
    • Kessler, DC; Taylor, JA; Dunson, DB, Learning phenotype densities conditional on many interacting predictors., Bioinformatics, vol 30 no. 11 (2014), pp. 1562-1568 [10.1093/bioinformatics/btu040] [abs].
    • Pati, D; Bhattacharya, A; Pillai, NS; Dunson, D, Posterior contraction in sparse Bayesian factor models for massive covariance matrices, Annals of Statistics, vol 42 no. 3 (2014), pp. 1102-1130 [10.1214/14-AOS1215] [abs].
    • Zhang, J; Jima, D; Moffitt, AB; Liu, Q; Czader, M; Hsi, ED; Fedoriw, Y; Dunphy, CH; Richards, KL; Gill, JI; Sun, Z; Love, C; Scotland, P; Lock, E; Levy, S; Hsu, DS; Dunson, D; Dave, SS, The genomic landscape of mantle cell lymphoma is related to the epigenetically determined chromatin state of normal B cells., Blood, vol 123 no. 19 (2014), pp. 2988-2996 [10.1182/blood-2013-07-517177] [abs].