Alan E. Gelfand

Alan E. Gelfand

James B. Duke Distinguished Emeritus Professor of Statistical Science

External address: 
223A Old Chem Bldg, Durham, NC 27708
Internal office address: 
Box 90251, Durham, NC 27708-0251
(919) 668-5229

Selected Grants

Double MOCHA: Phase II Multi-study Ocean acoustic Human effects Analysis awarded by (Co-Principal Investigator). 2018 to 2021

EMSW21-RTG: Geometric, Topological and Statistical Methods for Analyzing Massive Datasets awarded by National Science Foundation (Key Faculty). 2011 to 2018

Collaborative Research: Climate Change Impacts on Forest Biodiversity: Individual Risk to Subcontinental Impacts awarded by National Science Foundation (Co-Principal Investigator). 2012 to 2017

CDI-Type II: Integrating Algorithmic and Stochastic Modeling Techniques for Environmental Prediction awarded by National Science Foundation (Co-Principal Investigator). 2009 to 2014

Space-time Modeling for Linking Climate Change, Pollutant Exposure, Built Environments, and Health Outcomes awarded by North Carolina State University (Principal Investigator). 2011 to 2014

Dynamic Sensor Networks-Enabling the Measurement, Modeling, and Prediction of Biophysical Change in a Landscape awarded by National Science Foundation (Co-Principal Investigator). 2006 to 2012

Sharing Confidential Datasets With Geographic Identifiers Via Multiple Imputation awarded by National Institutes of Health (Co Investigator). 2009 to 2012

Bayesian Nonparametric Methods for Spatial and Spatiotemporal Data awarded by National Science Foundation (Principal Investigator). 2005 to 2009


Shirota, S., et al. “Analyzing car thefts and recoveries with connections to modeling origin–destination point patterns.” Spatial Statistics, vol. 38, Aug. 2020. Scopus, doi:10.1016/j.spasta.2020.100440. Full Text

Paci, L., et al. “Spatial hedonic modelling adjusted for preferential sampling.” Journal of the Royal Statistical Society. Series A: Statistics in Society, vol. 183, no. 1, Jan. 2020, pp. 169–92. Scopus, doi:10.1111/rssa.12489. Full Text

Gelfand, A. E. “Statistical challenges in spatial analysis of plant ecology data.” Spatial Statistics, Jan. 2020. Scopus, doi:10.1016/j.spasta.2020.100418. Full Text

Gelfand, A. E. “Introduction to the special issue on frontiers in spatial research.” Spatial Statistics, Jan. 2020. Scopus, doi:10.1016/j.spasta.2020.100423. Full Text

Hellmayr, C., and A. E. Gelfand. “A Partition Dirichlet Process Model for Functional Data Analysis.” Sankhya B, Jan. 2020. Scopus, doi:10.1007/s13571-019-00221-x. Full Text

Shen, Y., and A. E. Gelfand. “Exploring geometric anisotropy for point-referenced spatial data.” Spatial Statistics, vol. 32, Aug. 2019. Scopus, doi:10.1016/j.spasta.2019.100370. Full Text

White, P. A., et al. “Pollution state modelling for Mexico City.” Journal of the Royal Statistical Society. Series A: Statistics in Society, vol. 182, no. 3, June 2019, pp. 1039–60. Scopus, doi:10.1111/rssa.12444. Full Text

Gelfand, A. E., and S. Shirota. “Preferential sampling for presence/absence data and for fusion of presence/absence data with presence-only data.” Ecological Monographs, vol. 89, no. 3, Jan. 2019. Scopus, doi:10.1002/ecm.1372. Full Text

Shirota, Shinichiro, et al. “Spatial Joint Species Distribution Modeling using Dirichlet Processes.Statistica Sinica, vol. 29, no. 3, Jan. 2019, pp. 1127–54. Epmc, doi:10.5705/ss.202017.0482. Full Text

Wang, F., et al. “Process modeling for slope and aspect with application to elevation data maps.” Test, vol. 27, no. 4, Dec. 2018, pp. 749–72. Scopus, doi:10.1007/s11749-018-0619-x. Full Text