Scholars@Duke

• Background
• 

  Education, Training, & Certifications

  • Ph.D., Stanford University 1969
  • M.S., Stanford University 1967
  • B.S., City College of New York 1965
• 

  Previous Appointments & Affiliations

  • James B. Duke Distinguished Professor of Statistics and Decision Sciences, Statistical Science, Trinity College of Arts & Sciences 2002 - 2018
  • Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2002 - 2018
  • Professor in the Environmental Sciences and Policy Division, Nicholas School of the Environment, Duke University 2010 - 2013
  • Chair of the Department of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2007 - 2013
• Recognition
• 

  Awards & Honors

  • ENVR Distinguished Achievement Award (Medal). American Statistical Association. 2013
  • Highly Cited Researcher. Thomson Reuters. 2001
  • ASA Fellows. American Statistical Association. 1978
• Expertise
• 

  Global Scholarship

  • Research

    • United States of America (Country) 
• Research
• 

  Selected Grants

  • Improvements and innovations in marine mammal density modeling towards healthy oceans and diverse ecosystems awarded by National Oceanic and Atmospheric Administration 2020 - 2025
  • Double MOCHA: Phase II Multi-study Ocean acoustic Human effects Analysis awarded by University of St. Andrews 2018 - 2023
  • EAGER-NEON: Probabilistic forecasting of biodiversity response to intensifying drought: A proposal to combine NEON with national climate, species, and trait data bases awarded by  National Science Foundation 2015 - 2018
  • EMSW21-RTG: Geometric, Topological and Statistical Methods for Analyzing Massive Datasets awarded by  National Science Foundation 2011 - 2018
  • Collaborative Research: Climate Change Impacts on Forest Biodiversity: Individual Risk to Subcontinental Impacts awarded by  National Science Foundation 2012 - 2017
  • CDI-Type II: Integrating Algorithmic and Stochastic Modeling Techniques for Environmental Prediction awarded by  National Science Foundation 2009 - 2014
  • Space-time Modeling for Linking Climate Change, Pollutant Exposure, Built Environments, and Health Outcomes awarded by North Carolina State University 2011 - 2014
  • Dynamic Sensor Networks-Enabling the Measurement, Modeling, and Prediction of Biophysical Change in a Landscape awarded by  National Science Foundation 2006 - 2012
  • Sharing Confidential Datasets With Geographic Identifiers Via Multiple Imputation awarded by National Institutes of Health 2009 - 2012
  • Spatial Modeling of Air Pollution Exposure and Uncertainty with Application to Adverse Birth Outcomes awarded by Centers for Disease Control and Prevention 2008 - 2009
  • Bayesian Nonparametric Methods for Spatial and Spatiotemporal Data awarded by  National Science Foundation 2005 - 2009
  • Collaborative QEIB Research: Spatio-temporal Modeling of Species Distributions and Biodiversity-Integrating Climate and Population Responses awarded by  National Science Foundation 2005 - 2008
  • Duke Center for Geospatial Medicine awarded by National Institutes of Health 2004 - 2008
  • Optimal Spatial Design for Environmental Health Research awarded by National Institutes of Health 2005 - 2008
  • SCREMS: Distributed Environments for Stochastic Computation awarded by  National Science Foundation 2004 - 2007
  • The Valencia/ISBA Eighth World Meeting on Bayesian Statistics awarded by National Institutes of Health 2006 - 2007
  • The Valencia/ISBA Eighth World Meeting on Bayesian Statistics awarded by  National Science Foundation 2006 - 2007
  • The 2nd Summer Institute: Uncertainty in Ecological Inference, Forecasting, and Decision-Modern Statistical Computation awarded by  National Science Foundation 2005 - 2006
• Publications & Artistic Works
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  Selected Publications

  • Academic Articles

    • Hewitt, J., A. E. Gelfand, and R. S. Schick. “TIME-DISCRETIZATION APPROXIMATION ENRICHES CONTINUOUS-TIME DISCRETE-SPACE MODELS FOR ANIMAL MOVEMENT.” Annals of Applied Statistics 17, no. 1 (March 1, 2023): 740–60. https://doi.org/10.1214/22-AOAS1649.
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    • Tang, B., H. A. Frye, A. E. Gelfand, and J. A. Silander. “Zero-Inflated Beta Distribution Regression Modeling.” Journal of Agricultural, Biological, and Environmental Statistics 28, no. 1 (March 1, 2023): 117–37. https://doi.org/10.1007/s13253-022-00516-z.
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    • Tang, B., J. S. Clark, P. P. Marra, and A. E. Gelfand. “Modeling Community Dynamics Through Environmental Effects, Species Interactions and Movement.” Journal of Agricultural, Biological, and Environmental Statistics 28, no. 1 (March 1, 2023): 178–95. https://doi.org/10.1007/s13253-022-00520-3.
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    • Hewitt, J., A. E. Gelfand, N. J. Quick, W. R. Cioffi, B. L. Southall, S. L. DeRuiter, and R. S. Schick. “Kernel density estimation of conditional distributions to detect responses in satellite tag data.” Animal Biotelemetry 10, no. 1 (December 1, 2022). https://doi.org/10.1186/s40317-022-00299-7.
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    • Shirota, S., and A. E. Gelfand. “Preferential sampling for bivariate spatial data.” Spatial Statistics 51 (October 1, 2022). https://doi.org/10.1016/j.spasta.2022.100674.
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    • Castillo-Mateo, J., M. Lafuente, J. Asín, A. C. Cebrián, A. E. Gelfand, and J. Abaurrea. “Spatial Modeling of Day-Within-Year Temperature Time Series: An Examination of Daily Maximum Temperatures in Aragón, Spain.” Journal of Agricultural, Biological, and Environmental Statistics 27, no. 3 (September 1, 2022): 487–505. https://doi.org/10.1007/s13253-022-00493-3.
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    • Cebrián, A. C., J. Asín, A. E. Gelfand, E. M. Schliep, J. Castillo-Mateo, M. A. Beamonte, and J. Abaurrea. “Spatio-temporal analysis of the extent of an extreme heat event.” Stochastic Environmental Research and Risk Assessment 36, no. 9 (September 1, 2022): 2737–51. https://doi.org/10.1007/s00477-021-02157-z.
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    • White, P. A., H. Frye, M. F. Christensen, A. E. Gelfand, and J. A. Silander. “SPATIAL FUNCTIONAL DATA MODELING OF PLANT REFLECTANCES.” Annals of Applied Statistics 16, no. 3 (September 1, 2022): 1919–36. https://doi.org/10.1214/21-AOAS1576.
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    • Gelfand, A. E. “Spatial modeling for the distribution of species in plant communities.” Spatial Statistics 50 (August 1, 2022). https://doi.org/10.1016/j.spasta.2021.100582.
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    • Vihrs, N., J. Møller, and A. E. Gelfand. “Approximate Bayesian inference for a spatial point process model exhibiting regularity and random aggregation.” Scandinavian Journal of Statistics 49, no. 1 (March 1, 2022): 185–210. https://doi.org/10.1111/sjos.12509.
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    • Bravo, Mercedes A., Man Chong Leong, Alan E. Gelfand, and Marie Lynn Miranda. “Assessing Disparity Using Measures of Racial and Educational Isolation.” International Journal of Environmental Research and Public Health 18, no. 17 (September 2021): 9384. https://doi.org/10.3390/ijerph18179384.
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    • Tang, B., J. S. Clark, and A. E. Gelfand. “Modeling spatially biased citizen science effort through the eBird database.” Environmental and Ecological Statistics 28, no. 3 (September 1, 2021): 609–30. https://doi.org/10.1007/s10651-021-00508-1.
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    • White, P. A., and A. E. Gelfand. “Generalized Evolutionary Point Processes: Model Specifications and Model Comparison.” Methodology and Computing in Applied Probability 23, no. 3 (September 1, 2021): 1001–21. https://doi.org/10.1007/s11009-020-09797-8.
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    • White, P. A., and A. E. Gelfand. “Multivariate functional data modeling with time-varying clustering.” Test 30, no. 3 (September 1, 2021): 586–602. https://doi.org/10.1007/s11749-020-00733-z.
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    • Schliep, E. M., A. E. Gelfand, J. Abaurrea, J. Asín, M. A. Beamonte, and A. C. Cebrián. “Long-term spatial modelling for characteristics of extreme heat events.” Journal of the Royal Statistical Society. Series A: Statistics in Society 184, no. 3 (July 1, 2021): 1070–92. https://doi.org/10.1111/rssa.12710.
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    • Gelfand, A. E., and S. Shirota. “The role of odds ratios in joint species distribution modeling.” Environmental and Ecological Statistics 28, no. 2 (June 1, 2021): 287–302. https://doi.org/10.1007/s10651-021-00486-4.
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    • Hewitt, J., R. S. Schick, and A. E. Gelfand. “Continuous-Time Discrete-State Modeling for Deep Whale Dives.” Journal of Agricultural, Biological, and Environmental Statistics 26, no. 2 (June 1, 2021): 180–99. https://doi.org/10.1007/s13253-020-00422-2.
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    • Hellmayr, C., and A. E. Gelfand. “A Partition Dirichlet Process Model for Functional Data Analysis.” Sankhya B 83, no. 1 (May 1, 2021): 30–65. https://doi.org/10.1007/s13571-019-00221-x.
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    • Shirota, S., A. E. Gelfand, and J. Mateu. “Analyzing car thefts and recoveries with connections to modeling origin–destination point patterns.” Spatial Statistics 38 (August 1, 2020). https://doi.org/10.1016/j.spasta.2020.100440.
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    • Gelfand, A. E. “Introduction to the special issue on frontiers in spatial research.” Spatial Statistics 37 (June 1, 2020). https://doi.org/10.1016/j.spasta.2020.100423.
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    • Gelfand, A. E. “Statistical challenges in spatial analysis of plant ecology data.” Spatial Statistics 37 (June 1, 2020). https://doi.org/10.1016/j.spasta.2020.100418.
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    • Paci, L., A. E. Gelfand, M. A. Beamonte, P. Gargallo, and M. Salvador. “Spatial hedonic modelling adjusted for preferential sampling.” Journal of the Royal Statistical Society. Series A: Statistics in Society 183, no. 1 (January 1, 2020): 169–92. https://doi.org/10.1111/rssa.12489.
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    • 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 89, no. 3 (August 1, 2019). https://doi.org/10.1002/ecm.1372.
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    • Shen, Y., and A. E. Gelfand. “Exploring geometric anisotropy for point-referenced spatial data.” Spatial Statistics 32 (August 1, 2019). https://doi.org/10.1016/j.spasta.2019.100370.
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    • Wang, F., J. Wang, A. E. Gelfand, and F. Li. “Disease Mapping With Generative Models.” American Statistician 73, no. 3 (July 3, 2019): 213–23. https://doi.org/10.1080/00031305.2017.1392358.
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    • White, P. A., A. E. Gelfand, E. R. Rodrigues, and G. Tzintzun. “Pollution state modelling for Mexico City.” Journal of the Royal Statistical Society. Series A: Statistics in Society 182, no. 3 (June 1, 2019): 1039–60. https://doi.org/10.1111/rssa.12444.
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    • Schliep, E. M., and A. E. Gelfand. “Velocities for spatio-temporal point patterns.” Spatial Statistics 29 (March 1, 2019): 204–25. https://doi.org/10.1016/j.spasta.2018.12.007.
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    • Shirota, Shinichiro, Alan E. Gelfand, and Sudipto Banerjee. “Spatial Joint Species Distribution Modeling using Dirichlet Processes.” Statistica Sinica 29, no. 3 (January 2019): 1127–54. https://doi.org/10.5705/ss.202017.0482.
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    • Wang, F., A. Bhattacharya, and A. E. Gelfand. “Rejoinder on: Process modeling for slope and aspect with application to elevation data maps.” Test 27, no. 4 (December 1, 2018): 783–86. https://doi.org/10.1007/s11749-018-0623-1.
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    • Wang, F., A. Bhattacharya, and A. E. Gelfand. “Process modeling for slope and aspect with application to elevation data maps.” Test 27, no. 4 (December 1, 2018): 749–72. https://doi.org/10.1007/s11749-018-0619-x.
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    • Lu, X., A. E. Gelfand, and D. M. Holland. “Local real-time forecasting of ozone exposure using temperature data.” Environmetrics 29, no. 7 (November 1, 2018). https://doi.org/10.1002/env.2509.
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    • Schliep, E. M., A. E. Gelfand, J. S. Clark, and R. Kays. “Joint Temporal Point Pattern Models for Proximate Species Occurrence in a Fixed Area Using Camera Trap Data.” Journal of Agricultural, Biological, and Environmental Statistics 23, no. 3 (September 1, 2018): 334–57. https://doi.org/10.1007/s13253-018-0327-8.
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    • Schliep, E. M., A. E. Gelfand, R. M. Mitchell, M. E. Aiello-Lammens, and J. A. Silander. “Assessing the joint behaviour of species traits as filtered by environment.” Methods in Ecology and Evolution 9, no. 3 (March 1, 2018): 716–27. https://doi.org/10.1111/2041-210X.12901.
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    • Schliep, Erin M., Alan E. Gelfand, and David M. Holland. “Alternating Gaussian Process Modulated Renewal Processes for Modeling Threshold Exceedances and Durations.” Stochastic Environmental Research and Risk Assessment : Research Journal 32, no. 2 (February 2018): 401–17. https://doi.org/10.1007/s00477-017-1417-9.
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    • Wang, Feifei, Jian Wang, Alan Gelfand, and Fan Li. “Accommodating the ecological fallacy in disease mapping in the absence of individual exposures.” Statistics in Medicine 36, no. 30 (December 2017): 4930–42. https://doi.org/10.1002/sim.7494.
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    • Paci, L., M. A. Beamonte, A. E. Gelfand, P. Gargallo, and M. Salvador. “Analysis of residential property sales using space–time point patterns.” Spatial Statistics 21 (August 1, 2017): 149–65. https://doi.org/10.1016/j.spasta.2017.06.007.
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    • Shirota, S., and A. E. Gelfand. “Approximate Bayesian Computation and Model Assessment for Repulsive Spatial Point Processes.” Journal of Computational and Graphical Statistics 26, no. 3 (July 3, 2017): 646–57. https://doi.org/10.1080/10618600.2017.1299627.
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    • Shirota, S., and A. E. Gelfand. “Space and circular time log Gaussian Cox processes with application to crime event data.” Annals of Applied Statistics 11, no. 2 (June 1, 2017): 481–503. https://doi.org/10.1214/16-AOAS960.
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    • Gelfand, Alan E., and Sudipto Banerjee. “Bayesian Modeling and Analysis of Geostatistical Data.” Annual Review of Statistics and Its Application 4 (March 2017): 245–66. https://doi.org/10.1146/annurev-statistics-060116-054155.
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    • Schliep, E. M., A. E. Gelfand, J. S. Clark, and B. J. Tomasek. “Biomass prediction using a density-dependent diameter distribution model.” Annals of Applied Statistics 11, no. 1 (March 1, 2017): 340–61. https://doi.org/10.1214/16-AOAS1007.
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    • Leininger, T. J., and A. E. Gelfand. “Bayesian inference and model assessment for spatial point patterns using posterior predictive samples.” Bayesian Analysis 12, no. 1 (January 1, 2017): 1–30. https://doi.org/10.1214/15-BA985.
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    • Paci, L., A. E. Gelfand, M. A. Beamonte, M. Rodrigues, and F. Pérez-Cabello. “Space-time modeling for post-fire vegetation recovery.” Stochastic Environmental Research and Risk Assessment 31, no. 1 (January 1, 2017): 171–83. https://doi.org/10.1007/s00477-015-1182-6.
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    • Taylor-Rodríguez, D., K. Kaufeld, E. M. Schliep, J. S. Clark, and A. E. Gelfand. “Joint species distribution modeling: Dimension reduction using Dirichlet processes.” Bayesian Analysis 12, no. 4 (January 1, 2017): 939–67. https://doi.org/10.1214/16-BA1031.
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    • Mastrantonio, G., A. E. Gelfand, and G. Jona Lasinio. “The wrapped skew Gaussian process for analyzing spatio-temporal data.” Stochastic Environmental Research and Risk Assessment 30, no. 8 (December 1, 2016): 2231–42. https://doi.org/10.1007/s00477-015-1163-9.
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    • Gelfand, A. E., and E. M. Schliep. “Spatial statistics and Gaussian processes: A beautiful marriage.” Spatial Statistics 18 (November 1, 2016): 86–104. https://doi.org/10.1016/j.spasta.2016.03.006.
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    • Datta, Abhirup, Sudipto Banerjee, Andrew O. Finley, and Alan E. Gelfand. “On nearest-neighbor Gaussian process models for massive spatial data.” Wiley Interdisciplinary Reviews. Computational Statistics 8, no. 5 (September 2016): 162–71. https://doi.org/10.1002/wics.1383.
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    • Mastrantonio, G., G. Jona Lasinio, and A. E. Gelfand. “Spatio-temporal circular models with non-separable covariance structure.” Test 25, no. 2 (June 1, 2016): 331–50. https://doi.org/10.1007/s11749-015-0458-y.
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    • Datta, A., S. Banerjee, A. O. Finley, and A. E. Gelfand. “Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets.” Journal of the American Statistical Association 111, no. 514 (April 2, 2016): 800–812. https://doi.org/10.1080/01621459.2015.1044091.
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    • Ghosh, S., K. Zhu, A. E. Gelfand, and J. S. Clark. “Joint Modeling of Climate Niches for Adult and Juvenile Trees.” Journal of Agricultural, Biological, and Environmental Statistics 21, no. 1 (March 1, 2016): 111–30. https://doi.org/10.1007/s13253-015-0238-x.
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    • Schliep, E. M., A. E. Gelfand, J. S. Clark, and K. Zhu. “Modeling change in forest biomass across the eastern US.” Environmental and Ecological Statistics 23, no. 1 (March 1, 2016): 23–41. https://doi.org/10.1007/s10651-015-0321-z.
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    • Terres, M. A., and A. E. Gelfand. “Spatial process gradients and their use in sensitivity analysis for environmental processes.” Journal of Statistical Planning and Inference 168 (January 1, 2016): 106–19. https://doi.org/10.1016/j.jspi.2015.07.003.
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    • Rundel, C. W., E. M. Schliep, A. E. Gelfand, and D. M. Holland. “A data fusion approach for spatial analysis of speciated PM2.5 across time.” Environmetrics 26, no. 8 (December 1, 2015): 515–25. https://doi.org/10.1002/env.2369.
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    • Schliep, E. M., A. E. Gelfand, and J. S. Clark. “Stochastic Modeling for Velocity of Climate Change.” Journal of Agricultural, Biological, and Environmental Statistics 20, no. 3 (September 23, 2015): 323–42. https://doi.org/10.1007/s13253-015-0210-9.
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    • Gelfand, A. E., and S. Banerjee. “Bayesian wombling: Finding rapid change in spatial maps.” Wiley Interdisciplinary Reviews: Computational Statistics 7, no. 5 (September 1, 2015): 307–15. https://doi.org/10.1002/wics.1360.
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    • Terres, M. A., and A. E. Gelfand. “Using spatial gradient analysis to clarify species distributions with application to South African protea.” Journal of Geographical Systems 17, no. 3 (July 1, 2015): 227–47. https://doi.org/10.1007/s10109-015-0215-5.
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    • Paci, L., A. E. Gelfand, and D. Cocchi. “Quantifying uncertainty for temperature maps derived from computer models.” Spatial Statistics 12 (May 1, 2015): 96–108. https://doi.org/10.1016/j.spasta.2015.03.005.
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    • Gelfand, A. E. “Comments on: Comparing and selecting spatial predictors using local criteria.” Test 24, no. 1 (March 1, 2015): 29–30. https://doi.org/10.1007/s11749-014-0412-4.
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    • Finley, A. O., S. Banerjee, and A. E. Gelfand. “spBayes for large univariate and multivariate point-referenced spatio-temporal data models.” Journal of Statistical Software 63, no. 13 (January 1, 2015): 1–28. https://doi.org/10.18637/jss.v063.i13.
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    • Wang, F., A. E. Gelfand, and G. Jona-Lasinio. “Joint spatio-temporal analysis of a linear and a directional variable: Space-time modeling of wave heights and wave directions in the adriatic sea.” Statistica Sinica 25, no. 1 (January 1, 2015): 25–39. https://doi.org/10.5705/ss.2013.204w.
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    • Brynjarsdóttir, J., and A. E. Gelfand. “On Covariate Importance for Regression Models with Multivariate Response.” Journal of Agricultural, Biological, and Environmental Statistics 19, no. 4 (December 1, 2014): 479–500. https://doi.org/10.1007/s13253-014-0187-9.
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    • Schliep, E. M., T. Q. Dong, A. E. Gelfand, and F. Li. “Modeling individual tree growth by fusing diameter tape and increment core data.” Environmetrics 25, no. 8 (December 1, 2014): 610–20. https://doi.org/10.1002/env.2324.
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    • Neelon, Brian, Alan E. Gelfand, and Marie Lynn Miranda. “A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores.” Journal of the Royal Statistical Society. Series C, Applied Statistics 63, no. 5 (November 2014): 737–61. https://doi.org/10.1111/rssc.12061.
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    • Wang, F., and A. E. Gelfand. “Modeling Space and Space-Time Directional Data Using Projected Gaussian Processes.” Journal of the American Statistical Association 109, no. 508 (October 2, 2014): 1565–80. https://doi.org/10.1080/01621459.2014.934454.
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    • Gelfand, A. E., and J. V. D. Monteiro. “Explaining return times for wildfires.” Journal of Statistical Theory and Practice 8, no. 3 (July 3, 2014): 534–45. https://doi.org/10.1080/15598608.2013.821047.
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    • Clark, James S., Alan E. Gelfand, Christopher W. Woodall, and Kai Zhu. “More than the sum of the parts: forest climate response from joint species distribution models.” Ecological Applications : A Publication of the Ecological Society of America 24, no. 5 (July 2014): 990–99. https://doi.org/10.1890/13-1015.1.
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    • Paiva, Thais, Avishek Chakraborty, Jerry Reiter, and Alan Gelfand. “Imputation of confidential data sets with spatial locations using disease mapping models.” Statistics in Medicine 33, no. 11 (May 2014): 1928–45. https://doi.org/10.1002/sim.6078.
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    • Allen, Jenica M., Maria A. Terres, Toshio Katsuki, Kojiro Iwamoto, Hiromi Kobori, Hiroyoshi Higuchi, Richard B. Primack, Adam M. Wilson, Alan Gelfand, and John A. Silander. “Modeling daily flowering probabilities: expected impact of climate change on Japanese cherry phenology.” Global Change Biology 20, no. 4 (April 2014): 1251–63. https://doi.org/10.1111/gcb.12364.
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    • Allen, J. M., M. A. Terres, T. Katsuki, K. Iwamoto, H. Kobori, H. Higuchi, R. B. Primack, A. M. Wilson, A. Gelfand, and J. A. Silander. “Modeling daily flowering probabilities: Expected impact of climate change on Japanese cherry phenology.” Global Change Biology, 2014.
    • Allen, J. M., M. A. Terres, T. Katsuki, K. Iwamoto, H. Kobori, H. Higuchi, R. B. Primack, A. M. Wilson, A. Gelfand, and J. A. Silander. “Modeling daily flowering probabilities: Expected impact of climate change on Japanese cherry phenology.” Global Change Biology 20, no. 4 (2014): 1251–63.
    • Berrocal, V. J., A. E. Gelfand, and D. M. Holland. “Assessing exceedance of ozone standards: A space-time downscaler for fourth highest ozone concentrations.” Environmetrics 25, no. 4 (January 1, 2014): 279–91. https://doi.org/10.1002/env.2273.
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    • Ghosh, S., D. M. Bell, J. S. Clark, A. E. Gelfand, and P. G. Flikkema. “Process modeling for soil moisture using sensor network data.” Statistical Methodology 17, no. C (January 1, 2014): 99–112. https://doi.org/10.1016/j.stamet.2013.08.002.
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    • Nguyen, X., and A. E. Gelfand. “Bayesian nonparametric modeling for functional analysis of variance.” Annals of the Institute of Statistical Mathematics 66, no. 3 (January 1, 2014): 495–526. https://doi.org/10.1007/s10463-013-0436-7.
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    • Zhu, Kai, Christopher W. Woodall, Souparno Ghosh, Alan E. Gelfand, and James S. Clark. “Dual impacts of climate change: forest migration and turnover through life history.” Global Change Biology 20, no. 1 (January 2014): 251–64. https://doi.org/10.1111/gcb.12382.
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    • Allen, J. M., T. J. Leininger, J. D. Hurd, D. L. Civco, A. E. Gelfand, and J. A. Silander. “Socioeconomics drive woody invasive plant richness in New England, USA through forest fragmentation.” Landscape Ecology 28, no. 9 (November 1, 2013): 1671–86. https://doi.org/10.1007/s10980-013-9916-7.
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    • Berrocal, V. J., M. L. Miranda, A. E. Gelfand, and S. Bhattacharya. “Synthesizing categorical datasets to enhance inference.” Statistical Methodology 15 (November 1, 2013): 25–45. https://doi.org/10.1016/j.stamet.2013.04.001.
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    • Gelfand, A. E., S. Ghosh, and J. S. Clark. “Scaling integral projection models for analyzing size demography.” Statistical Science 28, no. 4 (November 1, 2013): 641–58. https://doi.org/10.1214/13-STS444.
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    • Leininger, T. J., A. E. Gelfand, J. M. Allen, and J. A. Silander. “Spatial Regression Modeling for Compositional Data With Many Zeros.” Journal of Agricultural, Biological, and Environmental Statistics 18, no. 3 (September 1, 2013): 314–34. https://doi.org/10.1007/s13253-013-0145-y.
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    • Terres, M. A., A. E. Gelfand, J. M. Allen, and J. A. Silander. “Analyzing first flowering event data using survival models with space and time-varying covariates.” Environmetrics 24, no. 5 (August 1, 2013): 317–31. https://doi.org/10.1002/env.2211.
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    • Wang, Fangpo, and Alan E. Gelfand. “Directional data analysis under the general projected normal distribution.” Statistical Methodology 10, no. 1 (July 2013): 113–27. https://doi.org/10.1016/j.stamet.2012.07.005.
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    • Paci, Lucia, Alan E. Gelfand, and David M. Holland. “Spatio-temporal modeling for real-time ozone forecasting.” Spatial Statistics 4 (May 2013): 79–93. https://doi.org/10.1016/j.spasta.2013.04.003.
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    • Chakraborty, A., M. A. Beamonte, A. E. Gelfand, M. P. Alonso, P. Gargallo, and M. Salvador. “Spatial interaction models with individual-level data for explaining labor flows and developing local labor markets.” Computational Statistics and Data Analysis 58, no. 1 (February 1, 2013): 292–307. https://doi.org/10.1016/j.csda.2012.08.016.
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    • Allen, J. M., T. J. Leininger, J. D. Hurd Jr, D. L. Civco, A. E. Gelfand, and J. A. Silander Jr. “Socioeconomics drive woody invasive plant richness in New England, USA through forest fragmentation.” Landscape Ecology, 2013, 1–16.
    • Nguyen, X., and A. E. Gelfand. “Bayesian nonparametric modeling for functional analysis of variance.” Annals of the Institute of Statistical Mathematics, 2013, 1–32.
    • Ghosh, S., A. E. Gelfand, and J. S. Clark. “Rejoinder.” Journal of Agricultural, Biological, and Environmental Statistics 17, no. 4 (December 1, 2012): 693–99. https://doi.org/10.1007/s13253-012-0124-8.
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    • Heaton, Matthew J., and Alan E. Gelfand. “Kernel Averaged Predictors for Spatio-Temporal Regression Models.” Spatial Statistics 2 (December 2012): 15–32. https://doi.org/10.1016/j.spasta.2012.05.001.
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    • Ghosh, Souparno, Alan E. Gelfand, and James S. Clark. “Inference for Size Demography from Point Pattern Data using Integral Projection Models.” Journal of Agricultural, Biological, and Environmental Statistics 17, no. 4 (December 2012). https://doi.org/10.1007/s13253-012-0123-9.
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    • Gelfand, Alan E., Sujit K. Sahu, and David M. Holland. “On the Effect of Preferential Sampling in Spatial Prediction.” Environmetrics 23, no. 7 (November 2012): 565–78. https://doi.org/10.1002/env.2169.
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    • Lum, K., and A. E. Gelfand. “Rejoinder.” Bayesian Analysis 7, no. 2 (September 10, 2012): 273–76. https://doi.org/10.1214/12-BA708REJ.
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    • Berrocal, Veronica J., Alan E. Gelfand, and David M. Holland. “Space-time data fusion under error in computer model output: an application to modeling air quality.” Biometrics 68, no. 3 (September 2012): 837–48. https://doi.org/10.1111/j.1541-0420.2011.01725.x.
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    • Ghosh, Souparno, Alan E. Gelfand, Kai Zhu, and James S. Clark. “The k-ZIG: flexible modeling for zero-inflated counts.” Biometrics 68, no. 3 (September 2012): 878–85. https://doi.org/10.1111/j.1541-0420.2011.01729.x.
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    • Heaton, M. J., S. C. Gray, and A. E. Gelfand. “Process modelling for contingency tables with ordered categories.” Statistical Modelling 12, no. 3 (June 1, 2012): 211–28. https://doi.org/10.1177/1471082X1001200301.
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    • Lum, Kristian, and Alan E. Gelfand. “Spatial Quantile Multiple Regression Using the Asymmetric Laplace Process.” Bayesian Analysis 7, no. 2 (June 1, 2012). https://doi.org/10.1214/12-ba708.
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    • Gelfand, Alan E. “Hierarchical Modeling for Spatial Data Problems.” Spatial Statistics 1 (May 2012): 30–39. https://doi.org/10.1016/j.spasta.2012.02.005.
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    • Swamy, Geeta K., Sharon Edwards, Alan Gelfand, Sherman A. James, and Marie Lynn Miranda. “Maternal age, birth order, and race: differential effects on birthweight.” J Epidemiol Community Health 66, no. 2 (February 2012): 136–42. https://doi.org/10.1136/jech.2009.088567.
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    • Finley, A. O., S. Banerjee, and A. E. Gelfand. “Bayesian dynamic modeling for large space-time datasets using Gaussian predictive processes.” Journal of Geographical Systems 14, no. 1 (January 1, 2012): 29–47. https://doi.org/10.1007/s10109-011-0154-8.
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    • Ghosh, S., A. E. Gelfand, and T. Mølhave. “Attaching uncertainty to deterministic spatial interpolations.” Statistical Methodology 9, no. 1–2 (January 1, 2012): 251–64. https://doi.org/10.1016/j.stamet.2011.06.001.
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    • Jona-Lasinio, G., A. Gelfand, and M. Jona-Lasinio. “Spatial analysis of wave direction data using wrapped Gaussian processes.” Annals of Applied Statistics 6, no. 4 (January 1, 2012): 1478–98. https://doi.org/10.1214/12-AOAS576.
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    • Guhaniyogi, Rajarshi, Andrew O. Finley, Sudipto Banerjee, and Alan E. Gelfand. “Adaptive Gaussian Predictive Process Models for Large Spatial Datasets.” Environmetrics 22, no. 8 (December 2011): 997–1007. https://doi.org/10.1002/env.1131.
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    • Anthopolos, R., S. A. James, A. E. Gelfand, and M. L. Miranda. “A Spatial Measure of Neighborhood Level Racial Isolation Applied to Low Birthweight, Preterm Birth, and Birthweight in North Carolina.” Spatial and Spatio Temporal Epidemiology 2, no. 4 (December 2011): 235–46.
    • Lum, K., and A. E. Gelfand. “Local linear suppression for wireless sensor network data.” Brazilian Journal of Probability and Statistics 25, no. 3 (November 1, 2011): 392–405. https://doi.org/10.1214/11-BJPS151.
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    • Wilson, A. M., A. M. Latimer, J. A. Silander, A. E. Gelfand, and H. de Klerk. “Corrigendum to "A Hierarchical Bayesian model of wildfire in a Mediterranean biodiversity hotspot: Implications of weather variability and global circulation" [Ecol. Modell. 221 (January (1)) (2010) 106-112].” Ecological Modelling 222, no. 18 (September 24, 2011): 3456. https://doi.org/10.1016/j.ecolmodel.2011.08.022.
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    • Clark, James S., Pankaj Agarwal, David M. Bell, Paul G. Flikkema, Alan Gelfand, Xuanlong Nguyen, Eric Ward, and Jun Yang. “Inferential ecosystem models, from network data to prediction.” Ecological Applications : A Publication of the Ecological Society of America 21, no. 5 (July 2011): 1523–36. https://doi.org/10.1890/09-1212.1.
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    • Gray, Simone C., Alan E. Gelfand, and Marie Lynn Miranda. “Hierarchical spatial modeling of uncertainty in air pollution and birth weight study.” Statistics in Medicine 30, no. 17 (July 2011): 2187–98. https://doi.org/10.1002/sim.4234.
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    • Nguyen, X. L., and A. E. Gelfand. “The dirichlet labeling process for clustering functional data.” Statistica Sinica 21, no. 3 (July 1, 2011): 1249–89. https://doi.org/10.5705/ss.2008.285.
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    • Heaton, M. J., and A. E. Gelfand. “Spatial Regression Using Kernel Averaged Predictors.” Journal of Agricultural, Biological, and Environmental Statistics 16, no. 2 (June 9, 2011): 233–52. https://doi.org/10.1007/s13253-010-0050-6.
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    • Berrocal, Veronica J., Alan E. Gelfand, David M. Holland, Janet Burke, and Marie Lynn Miranda. “On the use of a PM(2.5) exposure simulator to explain birthweight.” Environmetrics 22, no. 4 (June 2011): 553–71. https://doi.org/10.1002/env.1086.
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    • Kim, Dohyeong, Marie Lynn Miranda, Joshua Tootoo, Phil Bradley, and Alan E. Gelfand. “Spatial modeling for groundwater arsenic levels in North Carolina.” Environmental Science & Technology 45, no. 11 (June 2011): 4824–31. https://doi.org/10.1021/es103336s.
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    • Gelfand, A. E., and S. Banerjee. “Discussion on "Spatial prediction in the presence of positional error".” Environmetrics 22, no. 2 (March 1, 2011): 128. https://doi.org/10.1002/env.1093.
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    • Wilson, A. M., J. A. Silander, A. Gelfand, and J. H. Glenn. “Scaling up: Linking field data and remote sensing with a hierarchical model.” International Journal of Geographical Information Science 25, no. 3 (March 1, 2011): 509–21. https://doi.org/10.1080/13658816.2010.522779.
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    • Chakraborty, A., A. E. Gelfand, A. M. Wilson, A. M. Latimer, and J. A. Silander. “Point pattern modelling for degraded presence-only data over large regions.” Journal of the Royal Statistical Society. Series C: Applied Statistics 60, no. 5 (January 1, 2011): 757–76. https://doi.org/10.1111/j.1467-9876.2011.00769.x.
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    • Berrocal, Veronica J., Alan E. Gelfand, and David M. Holland. “A bivariate space-time downscaler under space and time misalignment.” The Annals of Applied Statistics 4, no. 4 (December 2010): 1942–75. https://doi.org/10.1214/10-aoas351.
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    • Chakraborty, A., and A. E. Gelfand. “Analyzing spatial point patterns subject to measurement error.” Bayesian Analysis 5, no. 1 (December 1, 2010): 97–122. https://doi.org/10.1214/10-BA504.
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    • Chakraborty, A., A. E. Gelfand, A. M. Wilson, A. M. Latimer, and J. A. Silander. “Modeling large scale species abundance with latent spatial processes.” Annals of Applied Statistics 4, no. 3 (September 1, 2010): 1403–29. https://doi.org/10.1214/10-AOAS335.
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    • Schwartz, Scott L., Alan E. Gelfand, and Marie L. Miranda. “Joint Bayesian analysis of birthweight and censored gestational age using finite mixture models.” Statistics in Medicine 29, no. 16 (July 2010): 1710–23. https://doi.org/10.1002/sim.3900.
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    • Berrocal, Veronica J., Alan E. Gelfand, and David M. Holland. “A Spatio-Temporal Downscaler for Output From Numerical Models.” Journal of Agricultural, Biological, and Environmental Statistics 15, no. 2 (June 2010): 176–97. https://doi.org/10.1007/s13253-009-0004-z.
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    • Rodríguez, Abel, David B. Dunson, and Alan E. Gelfand. “Latent Stick-Breaking Processes.” Journal of the American Statistical Association 105, no. 490 (April 2010): 647–59. https://doi.org/10.1198/jasa.2010.tm08241.
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    • Sang, H., and A. E. Gelfand. “Continuous spatial process models for spatial extreme values.” Journal of Agricultural, Biological, and Environmental Statistics 15, no. 1 (January 28, 2010): 49–65. https://doi.org/10.1007/s13253-009-0010-1.
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    • Wilson, A. M., A. M. Latimer, J. A. Silander, A. E. Gelfand, and H. de Klerk. “A Hierarchical Bayesian model of wildfire in a Mediterranean biodiversity hotspot: Implications of weather variability and global circulation.” Ecological Modelling 221, no. 1 (January 10, 2010): 106–12. https://doi.org/10.1016/j.ecolmodel.2009.09.016.
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    • Puggioni, G., and A. E. Gelfand. “Analyzing space-time sensor network data under suppression and failure in transmission.” Statistics and Computing 20, no. 4 (January 1, 2010): 409–19. https://doi.org/10.1007/s11222-009-9133-z.
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    • Sahu, S. K., A. E. Gelfand, and D. M. Holland. “Fusing point and areal level space-time data with application to wet deposition.” Journal of the Royal Statistical Society. Series C: Applied Statistics 59, no. 1 (January 1, 2010): 77–103. https://doi.org/10.1111/j.1467-9876.2009.00685.x.
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    • Tassone, Eric C., Marie Lynn Miranda, and Alan E. Gelfand. “Disaggregated spatial modelling for areal unit categorical data.” Journal of the Royal Statistical Society. Series C, Applied Statistics 59, no. 1 (January 2010): 175–90. https://doi.org/10.1111/j.1467-9876.2009.00682.x.
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    • Miranda, Marie Lynn, Geeta K. Swamy, Sharon Edwards, Pamela Maxson, Alan Gelfand, and Sherman James. “Disparities in maternal hypertension and pregnancy outcomes: evidence from North Carolina, 1994-2003.” Public Health Rep 125, no. 4 (2010): 579–87. https://doi.org/10.1177/003335491012500413.
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    • Duan, J. A., A. E. Gelfand, and C. F. Sirmansz. “Modeling space-time data using stochastic differential equations.” Bayesian Analysis 4, no. 4 (December 1, 2009): 733–58. https://doi.org/10.1214/09-BA427.
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    • Petrone, S., M. Guindani, and A. E. Gelfand. “Hybrid dirichlet mixture models for functional data.” Journal of the Royal Statistical Society. Series B: Statistical Methodology 71, no. 4 (September 1, 2009): 755–82. https://doi.org/10.1111/j.1467-9868.2009.00708.x.
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    • Finley, Andrew O., Huiyan Sang, Sudipto Banerjee, and Alan E. Gelfand. “Improving the performance of predictive process modeling for large datasets.” Computational Statistics & Data Analysis 53, no. 8 (June 2009): 2873–84. https://doi.org/10.1016/j.csda.2008.09.008.
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    • Rodríguez, Abel, David B. Dunson, and Alan E. Gelfand. “Bayesian Nonparametric Functional Data Analysis Through Density Estimation.” Biometrika 96, no. 1 (January 2009): 149–62. https://doi.org/10.1093/biomet/asn054.
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    • Sang, H., and A. E. Gelfand. “Hierarchical modeling for extreme values observed over space and time.” Environmental and Ecological Statistics 16, no. 3 (January 1, 2009): 407–26. https://doi.org/10.1007/s10651-007-0078-0.
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    • Sang, H., A. E. Gelfand, C. Lennard, G. Hegerl, and B. Hewitson. “Interpreting self-organizing maps through space-time data models.” Annals of Applied Statistics 2, no. 4 (December 1, 2008): 1194–1216. https://doi.org/10.1214/08-AOAS174.
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    • Liang, Shengde, Bradley P. Carlin, and Alan E. Gelfand. “ANALYSIS OF MINNESOTA COLON AND RECTUM CANCER POINT PATTERNS WITH SPATIAL AND NONSPATIAL COVARIATE INFORMATION.” The Annals of Applied Statistics 3, no. 3 (October 2008): 943–62. https://doi.org/10.1214/09-aoas240.
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    • Rodríguez, A., D. B. Dunson, and A. E. Gelfand. “The nested Dirichlet process: Rejoinder.” Journal of the American Statistical Association 103, no. 483 (September 1, 2008): 1153–54. https://doi.org/10.1198/016214508000000616.
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    • Banerjee, Sudipto, Alan E. Gelfand, Andrew O. Finley, and Huiyan Sang. “Gaussian predictive process models for large spatial data sets.” Journal of the Royal Statistical Society. Series B, Statistical Methodology 70, no. 4 (September 2008): 825–48. https://doi.org/10.1111/j.1467-9868.2008.00663.x.
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    • Puggioni, Gavino, Alan E. Gelfand, and Joann G. Elmore. “Joint modeling of sensitivity and specificity.” Statistics in Medicine 27, no. 10 (May 2008): 1745–61. https://doi.org/10.1002/sim.3186.
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    • Rappold, Ana G., Alan E. Gelfand, and David M. Holland. “Modeling mercury deposition through latent space-time processes.” Journal of the Royal Statistical Society. Series C, Applied Statistics 57, no. 2 (April 2008): 187–205. https://doi.org/10.1111/j.1467-9876.2007.00608.x.
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    • Kottas, Athanasios, Jason A. Duan, and Alan E. Gelfand. “Modeling disease incidence data with spatial and spatio temporal dirichlet process mixtures.” Biometrical Journal. Biometrische Zeitschrift 50, no. 1 (February 2008): 29–42. https://doi.org/10.1002/bimj.200610375.
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    • Rodríguez, A., D. B. Dunson, and A. E. Gelfand. “The nested dirichlet process.” Journal of the American Statistical Association 103, no. 483 (January 1, 2008): 1131–54. https://doi.org/10.1198/016214508000000553.
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    • Duan, J. A., M. Guindani, and A. E. Gelfand. “Generalized spatial dirichlet process models.” Biometrika 94, no. 4 (December 1, 2007): 809–25. https://doi.org/10.1093/biomet/asm071.
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    • Barber, J. J., and A. E. Gelfand. “Hierarchical spatial modeling for estimation of population size.” Environmental and Ecological Statistics 14, no. 3 (September 1, 2007): 193–205. https://doi.org/10.1007/s10651-007-0021-4.
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    • Gelfand, A. E. “Guest Editorial: Spatial and spatio-temporal modeling in environmental and ecological statistics.” Environmental and Ecological Statistics 14, no. 3 (September 1, 2007): 191–92. https://doi.org/10.1007/s10651-007-0026-z.
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    • Gelfand, A. E., S. Banerjee, C. F. Sirmans, Y. Tu, and S. Eng Ong. “Multilevel modeling using spatial processes: Application to the Singapore housing market.” Computational Statistics and Data Analysis 51, no. 7 (April 1, 2007): 3567–79. https://doi.org/10.1016/j.csda.2006.11.019.
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    • Woodard, D. B., A. E. Gelfand, W. E. Barlow, and J. G. Elmore. “Performance assessment for radiologists interpreting screening mammography.” Statistics in Medicine 26, no. 7 (March 2007): 1532–51. https://doi.org/10.1002/sim.2633.
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    • Majumdar, A., and A. E. Gelfand. “Multivariate spatial modeling for geostatistical data using convolved covariance functions.” Mathematical Geology 39, no. 2 (February 1, 2007): 225–45. https://doi.org/10.1007/s11004-006-9072-6.
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    • Bhattacharya, Sourabh, Alan E. Gelfand, and Kent E. Holsinger. “Model fitting and inference under Latent Equilibrium Processes.” Statistics and Computing 17, no. 2 (January 2007): 193–208. https://doi.org/10.1007/s11222-006-9015-6.
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    • Sahu, Sujit K., Alan E. Gelfand, and David M. Holland. “High Resolution Space-Time Ozone Modeling for Assessing Trends.” Journal of the American Statistical Association 102, no. 480 (January 2007): 1221–34. https://doi.org/10.1198/016214507000000031.
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    • Banerjee, Sudipto, and Alan E. Gelfand. “Bayesian Wombling: Curvilinear Gradient Assessment Under Spatial Process Models.” Journal of the American Statistical Association 101, no. 476 (December 2006): 1487–1501. https://doi.org/10.1198/016214506000000041.
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    • Gelfand, A. E., J. A. Silander, S. Wu, A. Latimer, P. O. Lewis, A. G. Rebelo, and M. Holder. “Explaining species distribution patterns through hierarchical modeling.” Bayesian Analysis 1, no. 1 A (December 1, 2006): 41–92. https://doi.org/10.1214/06-BA102.
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    • Gelfand, A. E., J. A. Silander, S. Wu, A. Latimer, P. O. Lewis, A. G. Rebelo, and M. Holder. “Rejoinder.” Bayesian Analysis 1, no. 1 A (December 1, 2006): 103–4. https://doi.org/10.1214/06-BA102REJ.
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    • Paliwal, Prashni, and Alan E. Gelfand. “Estimating measures of diagnostic accuracy when some covariate information is missing.” Statistics in Medicine 25, no. 17 (September 2006): 2981–93. https://doi.org/10.1002/sim.2436.
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    • Clark, James S., and Alan E. Gelfand. “A future for models and data in environmental science.” Trends in Ecology & Evolution 21, no. 7 (July 2006): 375–80. https://doi.org/10.1016/j.tree.2006.03.016.
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    • Guindani, M., and A. E. Gelfand. “Smoothness properties and gradient analysis under spatial Dirichlet process models.” Methodology and Computing in Applied Probability 8, no. 2 (June 1, 2006): 159–89. https://doi.org/10.1007/s11009-006-8547-8.
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    • Xia, G., M. L. Miranda, and A. E. Gelfand. “Approximately optimal spatial design approaches for environmental health data.” Environmetrics 17, no. 4 (June 1, 2006): 363–85. https://doi.org/10.1002/env.775.
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    • Sahu, S. K., A. E. Gelfand, and D. M. Holland. “Spatio-temporal modeling of fine particulate matter.” Journal of Agricultural, Biological, and Environmental Statistics 11, no. 1 (March 1, 2006): 61–86. https://doi.org/10.1198/108571106X95746.
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    • Barber, J. J., A. E. Gelfand, and J. A. Silander. “Modelling map positional error to infer true feature location.” Canadian Journal of Statistics 34, no. 4 (January 1, 2006): 659–76. https://doi.org/10.1002/cjs.5550340407.
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    • Flikkema, P. G., P. K. Agarwal, J. S. Clark, C. Ellis, A. Gelfand, K. Munagala, and J. Yang. “Model-driven dynamic control of embedded wireless sensor networks.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3993 LNCS-III (January 1, 2006): 409–16. https://doi.org/10.1007/11758532_55.
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    • Gelfand, A. E., A. Latimer, S. Wu, and J. A. Silander Jr. “Building Statistical Models to Analyze Species Distributions.” Ecological Applications 16, no. 1 (2006): 33–50.
    • Majumdar, A., H. J. Munneke, A. E. Gelfand, S. Banerjee, and C. F. Slrmans. “Gradients in spatial response surfaces with application to urban land values.” Journal of Business and Economic Statistics 24, no. 1 (January 1, 2006): 77–90. https://doi.org/10.1198/073500105000000162.
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    • Paliwal, Prashni, Alan E. Gelfand, Linn Abraham, William Barlow, and Joann G. Elmore. “Examining accuracy of screening mammography using an event order model.” Statistics in Medicine 25, no. 2 (January 2006): 267–83. https://doi.org/10.1002/sim.2220.
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    • Gelfand, A. E., A. Kottas, and S. N. Maceachern. “Bayesian nonparametric spatial modeling with dirichlet process mixing.” Journal of the American Statistical Association 100, no. 471 (September 1, 2005): 1021–35. https://doi.org/10.1198/016214504000002078.
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    • Short, M., B. P. Carlin, and A. E. Gelfand. “Bivariate spatial process modeling for constructing indicator or intensity weighted spatial CDFs.” Journal of Agricultural, Biological, and Environmental Statistics 10, no. 3 (September 1, 2005): 259–75. https://doi.org/10.1198/108571105X58568.
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    • Gelfand, A. E., S. Banerjee, and D. Gamerman. “Spatial process modelling for univariate and multivariate dynamic spatial data.” Environmetrics 16, no. 5 (August 1, 2005): 465–79. https://doi.org/10.1002/env.715.
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    • Agarwal, D. K., J. A. Silander, A. E. Gelfand, R. E. Dewar, and J. G. Mickelson. “Tropical deforestation in Madagascar: Analysis using hierarchical, spatially explicit, Bayesian regression models.” Ecological Modelling 185, no. 1 (June 10, 2005): 105–31. https://doi.org/10.1016/j.ecolmodel.2004.11.023.
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    • Gelfand, A. E., and S. K. Sahu. “On model expansion, model contraction, identifiability and prior information: Two illustrative scenarios involving mismeasured variables - Comment.” Statistical Science 20, no. 2 (May 1, 2005): 130–31.
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    • Gustafson, P., A. E. Gelfand, S. K. Sahu, W. O. Johnson, T. E. Hanson, L. Joseph, and J. Lee. “On model expansion, model contraction, identifiability and prior Information: Two illustrative scenarios involving mismeasured variables.” Statistical Science 20, no. 2 (May 1, 2005): 111–40. https://doi.org/10.1214/088342305000000098.
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    • Majumdar, A., A. E. Gelfand, and S. Banerjee. “Spatio-temporal change-point modeling.” Journal of Statistical Planning and Inference 130, no. 1–2 (March 1, 2005): 149–66. https://doi.org/10.1016/j.jspi.2003.08.022.
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    • Gelfand, A. E., A. M. Schmidt, S. Wu, J. A. Silander, A. Latimer, and A. G. Rebelo. “Modelling species diversity through species level hierarchical modelling.” Journal of the Royal Statistical Society. Series C: Applied Statistics 54, no. 1 (February 8, 2005): 1–20. https://doi.org/10.1111/j.1467-9876.2005.00466.x.
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    • Agarwal, D. K., and A. E. Gelfand. “Slice sampling for simulation based fitting of spatial data models.” Statistics and Computing 15, no. 1 (January 1, 2005): 61–69. https://doi.org/10.1007/s11222-005-4790-z.
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    • Gelfand, A. E., A. M. Schmidt, S. Wu, J. A. Silander, A. Latimer, and A. G. Rebelo. “Explaining Species diversity Through species Level Hierarchical Modeling.” Applied Statistics, no. 65 (2005): 1–20.
    • Gelfand, A. E., D. Agarwal, J. Silander, J. Mickelson, and R. Dewar. “Relating Tropical Deforestation and Population Pressure through spatially Explicit Misaligned Bivariate Regression Models.” Ecological Modelling, no. 185 (2005): 105–31.
    • Gelfand, A. E., J. A. Silander Jr, S. Wu, A. Latimer, P. O. Lewis, A. G. Rebelo, and M. Holder. “Explaining Species Distribution Patterns Through Hierarchical Modeling.” Bayesian Analysis, no. 1 (2005): 1;41-92.
    • Gelfand, A. E., M. Short, and B. P. Carlin. “Covariate-adjusted Spatial CDF's for Air Pollutant Data.” Journal of Agricultural, Biological and Environmental Statistics, no. 10 (2005): 259–75.
    • Gelfand, A. E., S. Banerjee, and D. Gamerman. “Spatial Process Modelling for Univariate and Multivariate Dynamic spatial Data.” Environmetrics, no. 16 (2005): 1–15.
    • Valente, J., S. S. Wu, A. Gelfand, and C. F. Sirmans. “Apartment rent prediction using spatial modeling.” Journal of Real Estate Research 27, no. 1 (January 1, 2005): 105–36.
    • Gelfand, A. E., J. Valenti, S. Wu, and C. F. Sirmans. “Apartment Rent Predictions Using Spatial Modeling.” Journal of Real Estate Research, no. 27 (2005): 1,105-136.
    • Banerjee, S., A. E. Gelfand, J. R. Knight, and C. F. Sirmans. “Spatial Modeling of House Prices Using Normalized Distance-Weighted Sums of Stationary Processes.” Journal of Business and Economic Statistics 22, no. 2 (April 1, 2004): 206–13. https://doi.org/10.1198/073500104000000091.
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    • Gelfand, A. E. “Discussion of “statistical research: Some advice for beginners” by M. Hamada and R. Sitter.” American Statistician 58, no. 3 (January 1, 2004): 197–98. https://doi.org/10.1198/tas.2004.s8.
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    • Gelfand, A. E., A. M. Schmidt, S. Banerjee, C. F. Sirmans, M. Fuentes, D. Higdon, and B. Sansó. “Nonstationary multivariate process modeling through spatially varying coregionalization.” Test 13, no. 2 (January 1, 2004): 263–312. https://doi.org/10.1007/BF02595775.
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    • Gelfand, A. E., A. M. Schmidt, S. Banerjee, and C. F. Sirmans. “Nonstationary Multivariate Process Modelling through Spatially Varying Coregionalization (with discussion).” Test, no. 13 (2004): 2;1-50.
    • Gelfand, A. E., M. D. Ecker, J. R. Knight, and C. F. Sirmans. “The Dynamics of Location in Home Price.” Journal of Real Estate and Financial Economics, no. 29 (2004): 149–66.
    • Gelfand, A. E., S. Banerjee, J. R. Knight, and C. F. Sirmans. “Nonstationary Spatial Modeling through Normalized Distance-Weighted Sums of Stationary Processes.” Journal of Business and Economic Statistics, no. 22 (2004): 206–13.
    • Latimer, A. M., J. A. Silander, A. E. Gelfand, A. G. Rebelo, and D. M. Richardson. “Quantifying threats to biodiversity from invasive alien plants and other factors: A case study from the Cape Floristic Region.” South African Journal of Science 100, no. 1–2 (January 1, 2004): 81–86.
    • Gelfand, A. E. “Discussion to: Statistical Research: Some Advice for Beginners.” The American Statistician, no. 58 (2004): 197–99.
    • Schmidt, A. M., and A. E. Gelfand. “A Bayesian coregionalization approach for multivariate pollutant data.” Journal of Geophysical Research: Atmospheres 108, no. 24 (December 27, 2003). https://doi.org/10.1029/2002jd002905.
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    • Banerjee, S., A. E. Gelfand, and C. F. Sirmans. “Directional Rates of Change under Spatial Process Models.” Journal of the American Statistical Association 98, no. 464 (December 1, 2003): 946–54. https://doi.org/10.1198/C16214503000000909.
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    • Zhu, L., B. P. Carlin, and A. E. Gelfand. “Hierarchical regression with misaligned spatial data: Relating ambient ozone and pediatric asthma ER visits in Atlanta.” Environmetrics 14, no. 5 (August 1, 2003): 537–57. https://doi.org/10.1002/env.614.
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    • Ecker, M. D., and A. E. Gelfand. “Spatial modeling and prediction under stationary non-geometric range anistropy.” Environmental and Ecological Statistics 10, no. 2 (June 1, 2003): 165–78. https://doi.org/10.1023/A:1023600123559.
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    • Fu, Rongwei, Alan E. Gelfand, and Kent E. Holsinger. “Exact moment calculations for genetic models with migration, mutation, and drift.” Theoretical Population Biology 63, no. 3 (May 2003): 231–43. https://doi.org/10.1016/s0040-5809(03)00003-0.
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    • Vlachos, P. K., and A. E. Gelfand. “On the calibration of Bayesian model choice criteria.” Journal of Statistical Planning and Inference 111, no. 1–2 (February 1, 2003): 223–34. https://doi.org/10.1016/S0378-3758(02)00304-X.
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    • Banerjee, S., and A. E. Gelfand. “On smoothness properties of spatial processes.” Journal of Multivariate Analysis 84, no. 1 (January 1, 2003): 85–100. https://doi.org/10.1016/S0047-259X(02)00016-7.
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    • Gelfand, A. E., H. J. Kim, C. F. Sirmans, and S. Banerjee. “Spatial Modeling with spatially varying Coefficient Processes.” Journal of the American Statistical Association, no. 98 (2003): 387–96.
    • Gelfand, A. E., R. Fu, and K. Holsinger. “Exact Moment Calculations for Genetic Models.” Journal of Theoretical Population Biology, no. 63 (2003): 231–43.
    • Gelfand, A. E., and A. Kottas. “Bayesian Semiparametric Regression for Median Residual Life.” Scandinavian Journal of Statistics 30, no. 4 (January 1, 2003): 651–65. https://doi.org/10.1111/1467-9469.00356.
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    • Gelfand, A. E., and A. M. Schmidt. “A Bayesian Coregionalization Approach for Multivariate Pollutant Data.” Journal of Geophysical Research Atmosphere, no. 108 (2003): D24,8783.
    • Gelfand, A. E., and M. D. Ecker. “Spatial Modeling and Prediction Under Range Anisotropy.” Environmental and Ecological Statistics, no. 10 (2003): 165–78.
    • Gelfand, Alan E., and Penelope Vounatsou. “Proper multivariate conditional autoregressive models for spatial data analysis.” Biostatistics (Oxford, England) 4, no. 1 (January 2003): 11–25. https://doi.org/10.1093/biostatistics/4.1.11.
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    • Trevisani, M., and A. E. Gelfand. “Inequalities between expected marginal log-likelihoods, with implications for likelihood-based model complexity and comparison measures.” Canadian Journal of Statistics 31, no. 3 (January 1, 2003): 239–50. https://doi.org/10.2307/3316084.
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    • Vounatsou, P., T. Smith, and A. E. Gelfand. “Spatial modelling of gene frequencies in the presence of undetectable alleles.” Journal of Applied Statistics 30, no. 1 (January 1, 2003): 49–62. https://doi.org/10.1080/0266476022000018501.
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    • Agarwal, D. K., A. E. Gelfand, and S. Citron-Pousty. “Zero-inflated models with application to spatial count data.” Environmental and Ecological Statistics 9, no. 4 (December 1, 2002): 341–55. https://doi.org/10.1023/A:1020910605990.
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    • Agarwal, D. K., A. E. Gelfand, and J. A. Silander. “Investigating tropical deforestation using two-stage spatially misaligned regression models.” Journal of Agricultural, Biological, and Environmental Statistics 7, no. 3 (September 1, 2002): 420–39. https://doi.org/10.1198/108571102348.
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    • Kottas, Athanasios, Márcia D. Branco, and Alan E. Gelfand. “A nonparametric Bayesian modeling approach for cytogenetic dosimetry.” Biometrics 58, no. 3 (September 2002): 593–600. https://doi.org/10.1111/j.0006-341x.2002.00593.x.
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    • Gelfand, A. E., and A. Kottas. “A computational approach for full nonparametric Bayesian inference under Dirichlet process mixture models.” Journal of Computational and Graphical Statistics 11, no. 2 (August 26, 2002): 289–305. https://doi.org/10.1198/106186002760180518.
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    • Brooks, S. P., J. Smith, A. Vehtari, M. Plummer, M. Stone, C. P. Robert, D. M. Titterington, et al. “Discussion on the paper by Spiegelhalter, Best, Carlin and van der Linde.” Journal of the Royal Statistical Society. Series B: Statistical Methodology 64, no. 4 (January 1, 2002): 616–39.
    • Clapp, J. M., H. J. Kim, and A. E. Gelfand. “Predicting spatial patterns of house prices using LPR and Bayesian smoothing.” Real Estate Economics 30, no. 4 (January 1, 2002): 505–32. https://doi.org/10.1111/1540-6229.00048.
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    • Gelfand, A. E., C. P. Carlin, A. S. Mugglin, and L. Zhu. “Modeling spatiotemporally misaligned areal and point process environmental data.” Quantitative Methods for Current Environmental Issues 1 (2002): 150–51.
    • Gelfand, A., D. Agarwal, and S. Pousty. “Zero-inflated regression models for spatial count data.” Environmental and Ecological Statistics, 2002.
    • Gelfand, A., and A. Kottas. “A Computational Approach for Full Nonparametric Bayesian Inference in Single and Multiple Sample Problems.” Journal of Computational and Graphical Statistics 114 (2002): 289–305.
    • Gelfand, A., and M. Trevisani. “Discussion to "Bayesian measures of model complexity and fit.” J. Royal Statistical Soc. B (2002): 631.
    • Sfiridis, J. M., and A. E. Gelfand. “A survey of sampling-based bayesian analysis of financial data.” International Journal of Phytoremediation 21, no. 1 (January 1, 2002): 273–91. https://doi.org/10.1080/1350486022000026885.
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    • Sfiridis, James, and Alan Gelfand. “A survey of sampling-based Bayesian analysis of financial data.” Applied Mathematical Finance 9, no. 4 (2002): 273–91.
    • Gelfand, A. E., J. M. Clapp, and H. J. Kim. “Spatial Prediction of House Prices Using LPR and Bayesian Smoothing.” Real Estate Economics 30 (2002).
    • Gelfand, A., and F. Wang. “A simulation based approach to sample size determination under a given model and for separating models.” Statistical Science 17 (2002): 193–208.
    • Gelfand, A., and S. Banerjee. “Prediction, interpolation and regression for spatially misaligned data points.” Sankhya A 64 (2002): 227–45.
    • Gelfand, A. E., and A. Kottas. “Nonparametric Bayesian modeling for stochastic order.” Annals of the Institute of Statistical Mathematics 53, no. 4 (December 1, 2001): 865–76. https://doi.org/10.1023/A:1014629724913.
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    • Kottas, A., and A. E. Gelfand. “Bayesian semiparametric median regression modeling.” Journal of the American Statistical Association 96, no. 456 (December 1, 2001): 1458–68. https://doi.org/10.1198/016214501753382363.
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    • Gelfand, A. E., B. P. Carlin, and M. Trevisani. “On computation using Gibbs sampling for multilevel models.” Statistica Sinica 11, no. 4 (October 1, 2001): 981–1003.
    • Gelfand, A., B. P. Carlin, and L. Zhu. “Accommodating Scale Misalignment In Spatio-Temporal Data In: Bayesian Methods with Applications to Science.” Policy and Official Statistics, 2001, 81–90.
    • Gelfand, A., L. Zhu, and B. P. Carlin. “On the Change of Support Problem for Spatio-Temporal Data.” Biostatistics 20 (2001): 31–45.
    • Gelfand, A., and A. Kottas. “Modeling Variability Order: A Semiparametric Bayesian Approach.” Methodology and Computing in Applied Probability 31 (2001): 427–42.
    • Gelfand, A. E. “Gibbs Sampling.” Journal of the American Statistical Association 95, no. 452 (December 1, 2000): 1300–1304. https://doi.org/10.1080/01621459.2000.10474335.
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    • Gelfand, A. E., M. D. Ecker, C. Christiansen, T. J. McLaughlin, and S. B. Soumerai. “Conditional categorical response models with application to treatment of acute myocardial infarction.” Journal of the Royal Statistical Society. Series C: Applied Statistics 49, no. 2 (December 1, 2000): 171–86.
    • Christiansen, C. L., F. Wang, M. B. Barton, W. Kreuter, J. G. Elmore, A. E. Gelfand, and S. W. Fletcher. “Predicting the cumulative risk of false-positive mammograms.” Journal of the National Cancer Institute 92, no. 20 (October 2000): 1657–66. https://doi.org/10.1093/jnci/92.20.1657.
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    • Banerjee, S., A. E. Gelfand, and W. Polasek. “Geostatistical modelling for spatial interaction data with application to postal service performance.” Journal of Statistical Planning and Inference 90, no. 1 (September 1, 2000): 87–105. https://doi.org/10.1016/s0378-3758(00)00111-7.
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    • Mugglin, A. S., B. P. Carlin, and A. E. Gelfand. “Fully model-based approaches for spatially misaligned data.” Journal of the American Statistical Association 95, no. 451 (September 1, 2000): 877–87. https://doi.org/10.1080/01621459.2000.10474279.
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    • Gelfand, A. E. “Comment.” Statistical Science 15, no. 3 (January 1, 2000): 217–18.
    • Gelfand, A., A. Mugglin, and B. P. Carlin. “Fully Model Based Approaches for Misaligned Spatial Data.” Journal American Statistical Association 95 (2000): 877–87.
    • Gelfand, A., M. D. Ecker, C. Christiansen, S. B. Soumerai, and T. J. McLaughlin. “Conditional Categorical Response Models with Application to Treatment Compliance for and Survival of AMI Patients.” Applied Statistics 49 (2000): 171–86.
    • Gelfand, A., P. Vounatsou, and T. Smith. “Modeling of Multinominal data with latent structure: application to geographical mapping of human gene and haplotype frequencies.” Biostatistics 10 (2000): 177–89.
    • Gelfand, A. E., S. K. Ghosh, C. Christiansen, S. B. Soumerai, and T. J. McLaughlin. “Proportional hazards models: A latent competing risk approach.” Journal of the Royal Statistical Society. Series C: Applied Statistics 49, no. 3 (2000): 385–97.
    • Gelfand, A. E., and F. Wang. “Modelling the cumulative risk for a false-positive under repeated screening events.” Statistics in Medicine 19, no. 14 (2000): 1865–79. https://doi.org/10.1002/1097-0258(20000730)19:14<1865::AID-SIM512>3.0.CO;2-M.
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    • Gelfand, A. “Discussion to Bayesian Backfitting.” Statistical Science 15 (2000): 217–18.
    • Gelfand, A., S. K. Ghosh, C. Christiansen, S. B. Soumerai, and T. J. McLaughlin. “Proportional Hazards Models: A Latent Risks Approach.” Applied Statistics 49 (2000): 385–97.
    • Gelfand, A. E., and S. K. Sahu. “Identifiability, Improper Priors, and Gibbs Sampling for Generalized Linear Models.” Journal of the American Statistical Association 94, no. 445 (March 1, 1999): 247–53. https://doi.org/10.1080/01621459.1999.10473840.
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    • Ecker, M. D., and A. E. Gelfand. “Bayesian modeling and inference for geometrically anisotropic spatial data.” Mathematical Geology 31, no. 1 (January 1, 1999): 67–83. https://doi.org/10.1023/A:1007593314277.
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    • Gelfand, A., and S. Sahu. “Gibbs Sampling, Identifiability and Improper Priors in Generalized Linear Mixed Models.” Journal American Statistical Association 94 (1999): 247–53.
    • Carlin, B. P., J. B. Kadane, and A. E. Gelfand. “Approaches for optimal sequential decision analysis in clinical trials.” Biometrics 54, no. 3 (September 1998): 964–75. https://doi.org/10.2307/2533849.
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    • Dey, D. K., A. E. Gelfand, T. B. Swartz, and P. K. Vlachos. “A simulation-intensive approach for checking hierarchical models.” Test 7, no. 2 (January 1, 1998): 325–46. https://doi.org/10.1007/BF02565116.
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    • Gelfand, A. E., D. Dey, T. Swartz, and P. Vlachos. “Simulation Based Model Checking for Hierarchical Models.” Test 7 (1998): 325–46.
    • Gelfand, A. E., S. K. Ghosh, J. R. Knight, and C. F. Sirmans. “Spatio-temporal modeling of residential sales data.” Journal of Business and Economic Statistics 16, no. 3 (January 1, 1998): 312–21. https://doi.org/10.1080/07350015.1998.10524770.
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    • Gelfand, A. E., and S. K. Ghosh. “Model choice: A minimum posterior predictive loss approach.” Biometrika 85, no. 1 (January 1, 1998): 1–11. https://doi.org/10.1093/biomet/85.1.1.
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    • Gelfand, A. E., and S. K. Ghosh. “Latent Waiting Time Models for Bivariate Event Times with Censoring.” Sankhya B 60 (1998): 31–47.
    • Knight, J. R., C. F. Sirmans, A. E. Gelfand, and S. K. Ghosh. “Analyzing real estate data problems using the Gibbs sampler.” Real Estate Economics 26, no. 3 (January 1, 1998): 469–92. https://doi.org/10.1111/1540-6229.00753.
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    • Gelfand, A. E., and M. D. Ecker. “Modeling and Inference for Geometrically Anisotropic Spatial Data.” Mathematical Geology 31 (1998): 67–83.
    • Gelfand, A. E., and S. K. Ghosh. “A Latent Risk Approach for Modeling Individual Level Data Consisting of Multiple Event Times.” Journal of Statistical Research 32 (1998): 23–39.
    • Dey, D. K., A. E. Gelfand, and F. Peng. “Overdispersed generalized linear models.” Journal of Statistical Planning and Inference 64, no. 1 (October 30, 1997): 93–107. https://doi.org/10.1016/s0378-3758(96)00207-8.
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    • Gelfand, A. E., B. K. Mallick, and W. Polasek. “Broken biological size relationships: A truncated semiparametric regression approach with measurement error.” Journal of the American Statistical Association 92, no. 439 (September 1, 1997): 836–45. https://doi.org/10.1080/01621459.1997.10474039.
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    • Mukhopadhyay, S., and A. E. Gelfand. “Dirichlet Process Mixed Generalized Linear Models.” Journal of the American Statistical Association 92, no. 438 (June 1, 1997): 633–39. https://doi.org/10.1080/01621459.1997.10474014.
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    • Waller, L. A., B. P. Carlin, H. Xia, and A. E. Gelfand. “Hierarchical Spatio-Temporal Mapping of Disease Rates.” Journal of the American Statistical Association 92, no. 438 (June 1, 1997): 607–17. https://doi.org/10.1080/01621459.1997.10474012.
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    • Ecker, M. D., and A. E. Gelfand. “Bayesian Variogram Modeling for an Isotropic Spatial Process.” Journal of Agricultural, Biological, and Environmental Statistics 2, no. 4 (January 1, 1997): 347–69. https://doi.org/10.2307/1400508.
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    • Mallick, B. K., and A. E. Gelfand. “Semiparametric errors-in-variables models: A Bayesian approach.” Journal of Statistical Planning and Inference 52, no. 3 (July 1, 1996): 307–21. https://doi.org/10.1016/0378-3758(95)00139-5.
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    • Gelfand, A. E. “Comment.” Journal of the American Statistical Association 91, no. 434 (June 1, 1996): 551–52. https://doi.org/10.1080/01621459.1996.10476920.
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    • Gelfand, A. E. “Discussion to Empirical methods for combining likelihoods.” J. Amer. Statist. Assoc. 91 (1996): 551–52.
    • Gelfand, A. E., and J. Sfiridis. “Bayesian Analysis of Financial Event Study Data.” Advances in Econometrics 11 (1996): 25–62.
    • Gelfand, A. E., and P. Vlachos. “Issues in Bayesian Clinical Trial Design for Categorical Endpoint Models.” Proceedings of the Biometrics Section, 1996.
    • Gelfand, A. E., S. K. Sahu, and B. P. Carlin. “Efficient parametrisations for normal linear mixed models.” Biometrika 82, no. 3 (September 1, 1995): 479–88. https://doi.org/10.1093/biomet/82.3.479.
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    • Gelfand, A. E., and B. K. Mallick. “Bayesian analysis of proportional hazards models built from monotone functions.” Biometrics 51, no. 3 (September 1995): 843–52. https://doi.org/10.2307/2532986.
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    • Geland, A. E., B. Mallic, and D. Dey. “Modeling expert opinion: likelihoods under incomplete probabilistic specification.” J. Amer Statist. Assoc. 90 (1995): 598–604.
    • Gelfand, A. E., B. K. Mallick, and D. K. Dey. “Modeling expert opinion arising as a partial probabilistic specification.” Journal of the American Statistical Association 90, no. 430 (January 1, 1995): 598–604. https://doi.org/10.1080/01621459.1995.10476552.
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    • Gelfand, A. E., and B. Mallick. “Discussion to Assessment and Propagation of Model Uncertainty.” J. Royal Stat. Soc. B 57 (1995): 82–83.
    • Gelfand, A. E., and B. Mallick. “Bayesian analysis of semiparametric proportional hazards models.” Biometrics 51 (1995): 843–52.
    • Gelfand, A. E., and B. P. Carlin. “Comment.” Statistical Science 10, no. 1 (January 1, 1995): 43–46. https://doi.org/10.1214/ss/1177010125.
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    • Gelfand, A. E., and B. P. Carlin. “Comment on Bayesian Computation and Stochastic Systems.” Statistical Science 10 (1995): 43–46.
    • Gelfand, A. E., and S. Mukhopadhyay. “On Nonparametric Bayesian Inference for the Distribution of a Random Sample.” Canadian Journal of Statistics 23 (1995): 411–20.
    • Gelfand, A. E., and S. Sahu. “On Markov chain Monte Carlo acceleration.” Journal of Computational and Graphical Statistics 3 (1995): 261–76.
    • Mallick, B. K., and A. E. Gelfand. “Generalized linear models with unknown link functions.” Biometrika 81, no. 2 (June 1, 1994): 237–45. https://doi.org/10.1093/biomet/81.2.237.
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    • Gelfand, A. E., and D. Dey. “Bayesian model choice: asymptotic and exact calculations.” Journal Royal Statistical Society B 56 (1994): 501–14.
    • Gelfand, A. E., J. Pai, and N. Ravishanker. “Bayesian analysis of concurrent time series with application to regional IBM revenue data.” Journal of Forecasting 13 (1994): 463–79.
    • Carlin, B. P., and A. E. Gelfand. “Parametric likelihood inference for record breaking problems.” Biometrika 80, no. 3 (September 1, 1993): 507–15. https://doi.org/10.1093/biomet/80.3.507.
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    • Gelfand, A. E. “Discussion to the papers of Smith and Roberts, Besag and Green, and Gilks et al.” J. Royal Statistical Society B 55 (1993): 70–71.
    • Gelfand, A. E. “Discussion to Approximate Bayesian Inference with the Weighted Likelihood Bootstrap.” J. Royal Statistical Society B 56 (1993): 36–37.
    • Gelfand, A. E., A. Racine Poon, A. F. M. Smith, and J. Wakefield. “Bayesian analysis of population models using the Gibbs sampler.” Applied Statistics 43 (1993): 201–21.
    • Gelfand, A. E., and B. Carlin. “Meximum likelihood estimation for constrained or missing data models.” Canadian Journal of Statistics 21 (1993): 303–11.
    • Gelfand, A. E., and B. P. Carlin. “Maximum‐likelihood estimation for constrained‐ or missing‐data models.” Canadian Journal of Statistics 21, no. 3 (January 1, 1993): 303–11. https://doi.org/10.2307/3315756.
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    • Gelfand, A. E. “Discussion to "Inference from Iterative Simulation using Multiple Sequences" and "Practical Markov Chain Monte Carlo".” Statistical Science 7 (1993): 486–87.
    • Crabtree, B. F., A. E. Gelfand, W. L. Miller, S. Zyzanski, P. J. O’Connor, and D. Winsemius. “Categorical data analysis in primary care research: log-linear models.” Family Medicine 24, no. 2 (February 1992): 145–51.
    • Gelfand, A. E. “Comment.” Statistical Science 7, no. 4 (January 1, 1992): 486–87. https://doi.org/10.1214/ss/1177011139.
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    • Gelfand, A. E. “Discussion to Constrained Monte Carlo maximum likelihood for dependent data.” Journal Royal Statistical Society B 54 (1992): 690–91.
    • Gelfand, A. E., A. F. M. Smith, and T. M. Lee. “Bayesian analysis of constrained parameter and truncated data problems using gibbs sampling.” Journal of the American Statistical Association 87, no. 418 (January 1, 1992): 523–32. https://doi.org/10.1080/01621459.1992.10475235.
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    • Gelfand, A. E., A. F. M. Smith, and T. M. Lee. “Bayesian analysis of constrained parameter and truncated data problems.” Journal Amer. Stat. Assoc. 87 (1992): 523–32.
    • Gelfand, A. E., B. Carlin, and A. F. M. Smith. “Hierarchical Bayesian analysis of change point problems.” Applied Statistics 41 (1992): 389–405.
    • Gelfand, A. E., B. Crabtree, S. Zyzanski, W. L. Miller, P. J. O’Connor, and D. Winsemius. “Data analysis in primary care research: log linear models.” Family Medicine 24 (1992): 145–51.
    • Lange, N., B. P. Carlin, and A. E. Gelfand. “Rejoinder.” Journal of the American Statistical Association 87, no. 419 (January 1, 1992): 631–32. https://doi.org/10.1080/01621459.1992.10475261.
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    • Gelfand, A. E., N. Lange, and B. Carlin. “Hierarchical Bayes models for the progression of HIV infection using longitudinal CD4+ counts (with discussion).” Journal Amer. Stat. Assoc. 87 (1992): 615–32.
    • Gelfand, A. E., and A. F. M. Smith. “Bayesian statistics without tears: a sampling-resampling perspective.” American Statistician 46 (1992): 84–88.
    • Gelfand, E. D., A. E, J. Glaz, L. Kuo, and T. -. M. Lee. “Inference for the maximum cell probability under multinomial sampling.” Naval Research Logistics 39 (1992): 97–114.
    • Carlin, B. P., and A. E. Gelfand. “An iterative Monte Carlo method for nonconjugate Bayesian analysis.” Statistics and Computing 1, no. 2 (December 1, 1991): 119–28. https://doi.org/10.1007/BF01889986.
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    • Wakefield, J. C., A. E. Gelfand, and A. F. M. Smith. “Efficient generation of random variates via the ratio-of-uniforms method.” Statistics and Computing 1, no. 2 (December 1, 1991): 129–33. https://doi.org/10.1007/BF01889987.
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    • Gelfand, A. E., and L. Kuo. “Nonparametric Bayesian bioassay including ordered polytomous response.” Biometrika 78, no. 3 (September 1, 1991): 657–66. https://doi.org/10.1093/biomet/78.3.657.
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    • Gelfand, A. E., and A. F. M. Smith. “Gibbs sampling for marginal posterior expectations.” Communications in Statistics, Theory and Methods 20, no. 5 and 6 (1991): 1747–66.
    • Gelfand, A. E., and A. F. Smith. “Gibbs sampling for marginal posterior expectations.” Communications in Statistics  Theory and Methods 20, no. 5–6 (January 1, 1991): 1747–66. https://doi.org/10.1080/03610929108830595.
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    • Gelfand, A. E., and S. R. Dalal. “A note on overdispersed exponential families.” Biometrika 77, no. 1 (December 1, 1990): 55–64. https://doi.org/10.2307/2336049.
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    • Gelfand, Alan E., Susan E. Hills, Amy Racine-Poon, and Adrian F. M. Smith. “Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling.” Journal of the American Statistical Association 85, no. 412 (December 1990): 972–972. https://doi.org/10.2307/2289594.
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    • Gelfand, Alan E., and Adrian F. M. Smith. “Sampling-Based Approaches to Calculating Marginal Densities.” Journal of the American Statistical Association 85, no. 410 (June 1990): 398–398. https://doi.org/10.2307/2289776.
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    • Gelfand, A. E., S. E. Hills, A. Racine-Poon, and A. F. M. Smith. “Illustration of Bayesian inference in normal data models using Gibbs sampling.” Journal of the American Statistical Association 85, no. 412 (January 1, 1990): 972–85. https://doi.org/10.1080/01621459.1990.10474968.
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    • Gelfand, A. E., and A. F. M. Smith. “Sampling-based approaches to calculating marginal densities.” Journal of the American Statistical Association 85, no. 410 (January 1, 1990): 398–409. https://doi.org/10.1080/01621459.1990.10476213.
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    • Gelfand, A. E., and B. Carlin. “A Sample Reuse Method for Accurate Parametric Empirical Bayes Confidence Intervals.” Journal Royal Stat. Soc. Ser. B 53 (1990): 189–200.
    • Gelfand, A. E., and B. Carlin. “Approaches for Empirical Bayes Confidence Intervals.” Journal Amer. Stat. Assoc. 85 (1990): 105–14.
    • Gelfand, A. E., and D. K. Dey. “On measuring Bayesian robustness of contaminated classes of priors.” Statistics and Decisions 9 (1990): 63–80.
    • Dey, D. K., and A. E. Gelfand. “Improved estimation of a patterned covariance matrix.” Journal of Multivariate Analysis 31, no. 1 (January 1, 1989): 107–16. https://doi.org/10.1016/0047-259X(89)90053-5.
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    • Gelfand, A. E., and B. Carlin. “Apoproaches for Empirical Bayes Confidence Intervals for a Vector of Exponential Scale Parameters.” Computing Science and Statistics Interface, 1989, 485–98.
    • Gelfand, A. E., and S. Leeds. “Estimation for Dirichlet Mixed Models.” Naval Research Logistics Quart. 36, no. 2 (1989): 197–214.
    • Gelfand, A. E., and D. K. Dey. “On the estimation of a variance ratio.” Journal of Statistical Planning and Inference 19, no. 1 (January 1, 1988): 121–31. https://doi.org/10.1016/0378-3758(88)90057-2.
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    • Gelfand, A. E., and D. K. Dey. “Improved Estimation of a Disturbance Variance.” Journal of Econometrics 39 (1988): 387–95.
    • Gelfand, A. E., and D. K. Dey. “Improved Estimation of Variance Components in Mixed Models.” Commun. in Statistics A 17 (1988): 3313–31.
    • Gelfand, A. E., and D. K. Dey. “Improved estimation of the disturbance variance in a linear regression model.” Journal of Econometrics 39, no. 3 (January 1, 1988): 387–95. https://doi.org/10.1016/0304-4076(88)90065-6.
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    • Gelfand, A. E., A. Das Gupta, and D. K. Dey. “A new admissibility theorem with applications to estimation of survival and hazard rates and means in the scale parameter family.” Sankhya A 50, no. 2 (1988): 269–81.
    • Gelfand, A. E. “Reconsidering Inference in Conjoint Analysis.” Proceedings of Asa Business and Economics Section, 1987, 135–41.
    • Gelfand, A. E. “Mean Square Error Behavior for Prediction in Linear Regression.” Communications in Statistics 16, no. 6 (1987): 1717–24.
    • Gelfand, A. E. “Estimation in Parametric Mixture Families.” Contributions to the Theory and Application of Statistics, 1987, 379–96.
    • CATTIN, P., and A. GELFAND. “A NEW NONMETRIC CONJOINT METHOD - SOME PRELIMINARY-RESULTS.” Advances in Consumer Research 13 (1986): 460–62.
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    • Gelfand, A. E. “On attribute importance and relative market shares in conjoint analysis.” Proceedings of the Business and Economics Statistics Section, 1984, 243–48.
    • Gelfand, A. E., P. Cattin, and J. Danes. “A simple Bayesian procedure for estimation in a conjoint model.” J. of Marketing Research XX (1983): 29–35.
    • Gelfand, A. E. “Estimation in concentral distributions.” Commun. in Statist. 12, no. 4 (1983): 463–75.
    • Gelfand, A. E. “A behavioral summary for completely random nets.” Bulletin of Mathematical Biology 44, no. 3 (May 1, 1982): 309–20. https://doi.org/10.1007/BF02462282.
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    • Gelfand, A. E., and C. C. Walker. “On the character of an distance between states in a binary switching net.” Biological Cybernetics 43, no. 2 (January 1982): 79–86. https://doi.org/10.1007/bf00336970.
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    • Gelfand, A. E. “An estimation problem for Poisson processes.” Australian Journal of Statistics 23, no. 2 (1981): 1–8.
    • Gelfand, A. E. “An Estimation Problem with Poisson Processes.” Australian Journal of Statistics 23, no. 2 (January 1, 1981): 224–31. https://doi.org/10.1111/j.1467-842X.1981.tb00780.x.
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    • Gelfand, A. E., and P. Shuchman. “Can judges select cases of "No Precedential Value"?” Emory Law Review 29 (1980): 193–228.
    • Gelfand, A. E., and C. C. Walker. “A system theoretic approach to the management of complex organizations: Management by consensus and its interaction with other management strategies.” Behavioral Science 25, no. 4 (1980): 250–60.
    • Gelfand, A. E., and W. R. Stephenson. “A model for precedential value of appellate court decisions.” Proceedings of the Social Statistics Section, 1979, 559–63.
    • Walker, C. C., and A. E. Gelfand. “A system theoretic approach to the management of complex organizations: Management by exception, priority, and input span in a class of fixed‐structure models.” Behavioral Science 24, no. 2 (January 1, 1979): 112–20. https://doi.org/10.1002/bs.3830240205.
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    • Gelfand, A. E., and C. C. Walker. “A model for complex organization with applications to managerial strategy and advertising policy.” Proceedings of the Business and Economic Statistics Section, 1978, 232–36.
    • Gelfand, A. E., and J. E. Davis. “The jury list in Connecticut: Is the voter registration list truly representative?” Connecticut Bar Journal 52, no. 6 (1978): 449–74.
    • Gelfand, A. E. “Are 6-member juries really as good as 12-member juries?” Trial Magazine 13, no. 2 (1977): 41–43.
    • Gelfand, A. E. “Toward characterizing Boolean transformations.” Proceedings of the Society for General Systems Research, 1977, 111–16.
    • Gelfand, A. E., and C. C. Walker. “The distribution of cycle lengths in a class of abstract systems.” Internat. J. General Syst. 2, no. 4 (1977): 39–46.
    • Gelfand, A. E., and C. C. Walker. “The Distribution of Cycle Lengths in a Class of Abstract Systems.” International Journal of General Systems 4, no. 1 (January 1, 1977): 39–45. https://doi.org/10.1080/03081077708960654.
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    • Gelfand, A. E., and H. Solomon. “Rejoinder.” Journal of the American Statistical Association 72, no. 359 (January 1, 1977): 536–37. https://doi.org/10.1080/01621459.1977.10480609.
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    • Gelfand, A. E., and H. Solomon. “Considerations in building jury behavior models.” Jurimetrics Journal 17, no. 4 (1977): 292–314.
    • Gelfand, A. E., and H. Solomon. “Rejoinder to On the Effect of Jury Size.” J. Amer. Statist. Assoc. 72, no. 359 (1977): 536–37.
    • Gelfand, A. E. “COMPARISON OF SEVERAL ATTRIBUTE SAMPLING PLANS.” Naval Research Logistics 23, no. 3 (January 1, 1976): 513–23.
    • Gelfand, A. E., and D. L. Thomas. “Discrimination between the binomial and hypergeometric models.” Commun. in Statist. 5, no. 3 (1976): 225–40.
    • Gelfand, A. E., and H. Solomon. “analyzing the decision making process of the American jury.” J. Amer. Statist. Assoc. 70, no. 350 (1975): 305–10.
    • Gelfand, A. E. “Rapid seriation methods for multivariate observations through similarities.” Commun. in Statist. 3, no. 7 (1974): 635–45.
    • Gelfand, A. E., and D. L. Thomas. “Confidence coefficients for binomial interval estimates under hypergeometric models.” Amer. Statistician 28, no. 2 (1974): 52–56.
    • Gelfand, A. E., and H. Solomon. “Modeling jury verdicts for the American legal system.” J. Amer. Statist. Assoc. 69, no. 345 (1974): 32–38.
    • Gelfand, A. E., and H. Solomon. “A study of Poisson's models for jury verdicts in criminal and civil trials.” J. Amer. Statist. Assoc. 68, no. 342 (1973): 271–78.
    • Gelfand, A. E. “Rapid seriation methods with archaeological applications.” Mathematics in the Archaeological and Historical Sciences, 1971, 186–201.
    • Gelfand, A. E. “Seriation Methods for arthaeological materials.” American Antiquity 36, no. 3 (1971): 263–74.
    • White, P., A. E. Gelfand, and T. Utlaut. “Prediction and Model Comparison for Areal Unit Data.” Spatial Statistics (Submitted) (n.d.).

  • Book Sections

    • Gelfand, A. E. “Multivariate Spatial Process Models.” In Handbook of Regional Science: Second and Extended Edition: With 238 Figures and 78 Tables, 1985–2016, 2021. https://doi.org/10.1007/978-3-662-60723-7_120.
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    • Trevisani, M., and A. Gelfand. “Spatial misalignment models for small area estimation: A simulation study.” In Studies in Theoretical and Applied Statistics, Selected Papers of the Statistical Societies, 269–79, 2013. https://doi.org/10.1007/978-3-642-35588-2_25.
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    • Gelfand, A. E., S. Banerjee, and A. O. Finley. “Spatial Design for Knot Selection in Knot-Based Dimension Reduction Models.” In Spatio-Temporal Design: Advances in Efficient Data Acquisition, 142–69, 2012. https://doi.org/10.1002/9781118441862.ch7.
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    • Gelfand, A. E. “Gibbs sampling.” In Statistics in the 21st Century, 341–49, 2001.

  • Conference Papers

    • Ahn, S., M. Chertkov, A. E. Gelfand, S. Park, and J. Shin. “Maximum weight matching using odd-sized cycles: Max-product belief propagation and half-integrality.” In Ieee Transactions on Information Theory, 64:1471–80, 2018. https://doi.org/10.1109/TIT.2017.2788038.
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    • Caponera, A., F. Denti, T. Rigon, A. Sottosanti, and A. Gelfand. “Hierarchical spatio-temporal modeling of resting state fMRI data.” In Springer Proceedings in Mathematics and Statistics, 257:111–30, 2018. https://doi.org/10.1007/978-3-030-00039-4_7.
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    • Yu, R., A. Gelfand, S. Rajan, C. Shahabi, and Y. Liu. “Geographic segmentation via latent poisson factor model.” In Wsdm 2016  Proceedings of the 9th Acm International Conference on Web Search and Data Mining, 357–66, 2016. https://doi.org/10.1145/2835776.2835806.
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    • Pan, J., V. Rao, P. K. Agarwal, and A. E. Gelfand. “Markov-modulated marked poisson processes for check-in data.” In 33rd International Conference on Machine Learning, Icml 2016, 5:3311–20, 2016.
    • Gelfand, A. E., J. Shin, and M. Chertkov. “Belief propagation for linear programming.” In Ieee International Symposium on Information Theory  Proceedings, 2249–53, 2013. https://doi.org/10.1109/ISIT.2013.6620626.
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    • Chertkov, M., A. Gelfand, and J. Shin. “Loop calculus and bootstrap-belief propagation for perfect matchings on arbitrary graphs.” In Journal of Physics: Conference Series, Vol. 473, 2013. https://doi.org/10.1088/1742-6596/473/1/012007.
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    • Shin, J., A. E. Gelfand, and M. Chertkov. “A graphical transformation for belief propagation: Maximum Weight Matchings and odd-sized cycles.” In Advances in Neural Information Processing Systems, 2013.
    • Gelfand, A. E., and M. Welling. “Generalized belief propagation on tree robust structured region graphs.” In Uncertainty in Artificial Intelligence  Proceedings of the 28th Conference, Uai 2012, 296–305, 2012.
    • Welling, M., A. E. Gelfand, and A. Ihler. “A cluster-cumulant expansion at the fixed points of belief propagation.” In Uncertainty in Artificial Intelligence  Proceedings of the 28th Conference, Uai 2012, 883–92, 2012.
    • Chen, Y., A. Gelfand, C. C. Fowlkes, and M. Welling. “Integrating local classifiers through nonlinear dynamics on label graphs with an application to image segmentation.” In Proceedings of the Ieee International Conference on Computer Vision, 2635–42, 2011. https://doi.org/10.1109/ICCV.2011.6126553.
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    • Gelfand, A. E., K. Kask, and R. Dechter. “Stopping rules for randomized greedy triangulation schemes.” In Proceedings of the National Conference on Artificial Intelligence, 2:1043–48, 2011.
    • Kask, K., A. Gelfand, L. Otten, and R. Dechter. “Pushing the power of stochastic greedy ordering schemes for inference in graphical models.” In Proceedings of the National Conference on Artificial Intelligence, 1:54–60, 2011.
    • Gray, S. C., A. E. Gelfand, and M. L. Miranda. “SPATIAL ADJUSTMENT OF MEASUREMENT ERROR IN MATERNAL EXPOSURE TO AIR POLLUTION.” In American Journal of Epidemiology, 173:S153–S153. OXFORD UNIV PRESS INC, 2011.
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    • Gelfand, A. E., Y. Chen, M. Welling, and L. Van Der Maaten. “On herding and the perceptron cycling theorem.” In Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, Nips 2010, 2010.
    • Gray, S. C., A. E. Gelfand, and M. L. Miranda. “HIERARCHICAL SPATIAL MODELING OF UNCERTAINTY IN AIR POLLUTION AND BIRTH WEIGHT STUDY.” In American Journal of Epidemiology, 171:S79–S79. OXFORD UNIV PRESS INC, 2010.
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    • Kask, K., R. Dechter, and A. E. Gelfand. “Beem : BBucket Elimination with external memory.” In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, Uai 2010, 268–76, 2010.
    • Gelfand, A. E., C. Smith, M. Colony, and C. Bowman. “Performance evaluation of decentralized estimation systems with uncertain communication.” In 2009 12th International Conference on Information Fusion, Fusion 2009, 786–93, 2009.
    • Anthopolos, R., S. James, A. E. Gelfand, V. Berrocal, and M. L. Miranda. “A NEIGHBORHOOD AND SPATIAL MEASURE OF RACIAL ISOLATION APPLIED TO BIRTHWEIGHT.” In American Journal of Epidemiology, 169:S16–S16. OXFORD UNIV PRESS INC, 2009.
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    • Gelfand, A., C. Smith, M. Colony, C. Bowman, R. Pei, T. Huynh, and C. Brown. “Advanced algorithms for distributed fusion.” In Proceedings of Spie  the International Society for Optical Engineering, Vol. 6974, 2008. https://doi.org/10.1117/12.777635.
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    • Lee, K. D., A. E. Gelfand, E. Wiesenfeld, and B. Stepnitz. “Introduction of the hybrid inference tool (HIT).” In Fusion 2007  2007 10th International Conference on Information Fusion, 2007. https://doi.org/10.1109/ICIF.2007.4408116.
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    • Silberstein, A., R. Braynard, G. Filpus, G. Puggioni, A. Gelfand, K. Munagala, and J. Yang. “Data-driven processing in sensor networks.” In Cidr 2007  3rd Biennial Conference on Innovative Data Systems Research, 10–21, 2007.
    • Lee, K. D., E. Wiesenfeld, and A. Gelfand. “Statistical comparison of a hybrid approach with approximate and exact inference models for fusion 2+.” In Proceedings of Spie  the International Society for Optical Engineering, Vol. 6567, 2007. https://doi.org/10.1117/12.717398.
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    • Flikkema, P. G., P. K. Agarwal, J. S. Clark, C. Ellis, A. Gelfand, K. Munagala, and J. Yang. “From data reverence to data relevance: Model-mediated wireless sensing of the physical environment.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4487 LNCS:988–94, 2007. https://doi.org/10.1007/978-3-540-72584-8_130.
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    • Silberstein, A., G. Puggioni, A. Gelfand, K. Munagala, and J. Yang. “Suppression and failures in sensor networks: A Bayesian approach.” In 33rd International Conference on Very Large Data Bases, Vldb 2007  Conference Proceedings, 842–53, 2007.
    • Silberstein, Adam, Alan E. Gelfand, Kamesh Munagala, Gavino Puggioni, and Jun Yang. “Making Sense of Suppressions and Failures in Sensor Data: A Bayesian Approach.” In Vldb, edited by Christoph Koch, Johannes Gehrke, Minos N. Garofalakis, Divesh Srivastava, Karl Aberer, Anand Deshpande, Daniela Florescu, et al., 842–53. ACM, 2007.
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