David B. Dunson
- Arts and Sciences Professor of Statistical Science
- Professor of Statistical Science
- 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).
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
Hannah, LA, Powell, WB, and Dunson, DB. "Semiconvex Regression for Metamodeling-Based Optimization." SIAM Journal on Optimization 24.2 (January 2014): 573-597. Full Text
Chen, CWS, Dunson, D, Frühwirth-Schnatter, S, and Walker, SG. "Special issue on Bayesian computing, methods and applications." Computational Statistics and Data Analysis 71 (2014): 273--. Full Text
Scarpa, B, and Dunson, DB. "Enriched Stick Breaking Processes for Functional Data." Journal of the American Statistical Association 109.506 (January 2014): 647-660. 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.
Durante, D, Scarpa, B, and Dunson, DB. "Locally adaptive factor processes for multivariate time series." Journal of Machine Learning Research 15 (January 1, 2014): 1493-1522.
Carlson, DE, Vogelstein, JT, Qisong Wu, , Wenzhao Lian, , Mingyuan Zhou, , Stoetzner, CR, Kipke, D, Weber, D, Dunson, DB, and Carin, L. "Multichannel electrophysiological spike sorting via joint dictionary learning and mixture modeling." Ieee Transactions on Bio Medical Engineering 61.1 (January 2014): 41-54. Full Text Open Access Copy
Canale, A, and Dunson, DB. "Nonparametric Bayes modelling of count processes." BIOMETRIKA 100.4 (December 2013): 801-816. Full Text