David B. Dunson
- Arts and Sciences Professor of Statistical Science
- Professor of Statistical Science
- Professor in the Department of Electrical and Computer Engineering (Secondary)
- Faculty Network Member of the Duke Institute for Brain Sciences
Development of Bayesian statistical methods and approaches for uncertainty quantification motivated by applications with complex and high-dimensional data. A particular interest is in high-dimensional low sample size data in which it is necessary to incorporate dimensional reduction through carefully designed prior distributions and challenges arise in efficiently computing posterior approximations. Ongoing focus areas include new algorithms for approximating posterior distributions in big data settings, nonparametric Bayes probability modeling allowing for uncertainty in distributional assumptions, analysis of network data, incorporating physical and geometric prior knowledge in modeling and novel models for dimension reduction for "object data" (functions, tensors, shapes, etc). Primary application areas include genomics, neurosciences, epidemiology, and reproductive studies but with much broader interests in developing new methods motivated by difficult applications (in art, music, radar, imaging processing, etc).
Lin, L, and Dunson, DB. "Bayesian monotone regression using Gaussian process projection." Biometrika 101.2 (June 1, 2014): 303-317. Full Text
Kessler, DC, Taylor, JA, and Dunson, DB. "Learning phenotype densities conditional on many interacting predictors." Bioinformatics 30.11 (June 2014): 1562-1568. Full Text
Pati, D, Bhattacharya, A, Pillai, NS, and Dunson, D. "Posterior contraction in sparse Bayesian factor models for massive covariance matrices." The Annals of Statistics 42.3 (June 2014): 1102-1130. Full Text
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, and Dave, SS. "The genomic landscape of mantle cell lymphoma is related to the epigenetically determined chromatin state of normal B cells." Blood 123.19 (May 2014): 2988-2996. Full Text
Kundu, S, and Dunson, DB. "Bayes variable selection in semiparametric linear models." Journal of the American Statistical Association 109.505 (March 2014): 437-447. Full Text
Pati, D, and Dunson, DB. "Bayesian nonparametric regression with varying residual density." Annals of the Institute of Statistical Mathematics 66.1 (February 1, 2014): 1-31. Full Text
Cui, K, and Dunson, DB. "Generalized Dynamic Factor Models for Mixed-Measurement Time Series." Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 23.1 (February 2014): 169-191. Full Text
Bhattacharya, A, Pati, D, and Dunson, D. "Anisotropic function estimation using multi-bandwidth Gaussian processes." The Annals of Statistics 42.1 (February 2014): 352-381. Full Text
Xing, Z, Nicholson, B, Jimenez, M, Veldman, T, Hudson, L, Lucas, J, Dunson, D, Zaas, AK, Woods, CW, Ginsburg, GS, and Carin, L. "Bayesian modeling of temporal properties of infectious disease in a college student population." Journal of Applied Statistics 41.6 (January 1, 2014): 1358-1382. Full Text
Wade, S, Dunson, DB, Petrone, S, and Trippa, L. "Improving prediction from dirichlet process mixtures via enrichment." Journal of Machine Learning Research 15 (January 1, 2014): 1041-1071.