Sudipto Banerjee PhD » Faculty

Research Interests:Statistical analysis and modelling of geographically/spatially referenced datasets, Bayesian statistics (theory and methods), statistical computing and interface modelling with Geographical Information Systems.

Adjunct Professor, Biostatistics

Address Division of Biostatistics A430 Mayo Building, MMC 303 Minneapolis MN 55455 Work Phone: 612-624-0624 Website: http://www.biostat.umn.edu/~sudiptob
Photo of Sudipto Banerjee PhD

Biography:

Sudipto Banerjee received a Ph.D. and an M.S. in statistics from the University of Connecticut. His current interests include hierarchical modeling of data arising from spatial processes, interpolation and prediction (kriging) methods, and smoothness of spatial processes. Dr. Banerjee is also interested in modeling geographically referenced survival data. He is collaborating with plant geneticists, as well as researchers in epidemiology and environmental and occupational health, in designing and analyzing studies in such fields. Dr. Banerjee’s current research involves Bayesian Wombling methods that model stochastic gradients using GIS and formal statistics.

  • Ph.D., Statistics, University of Connecticut, 2000
  • M.S., Statistics, University of Connecticut, 1998
  • M.Stat., Indian Statistical Institute, Calcutta, India, 1996
  • B.S., Presidency College, Calcutta, India, 1994.

Research Expertise:

Bayesian Analysis, Biostatistics, Environmental Health, Global Health, Machine Learning, Spatial Statistics, Statistics Education

Honors:

  • 2005, Inductee: Pi Chapter of Delta Omega National Honor Society.
  • 2009, Abdel El Sharaawi Young Researcher Award from The International Environmetrics Society.
  • 2010, Elected member, International Statistical Institute.
  • 2011, Mortimer Spiegelman Award from the Statistics Section of the American Public Health Association.

Research Projects

Hierarchical Modeling Approaches for Geographical Boundary Analysis in Cancer Studies
Principal Investigator: Sudipto Banerjee
Funding Agency: NIH National Cancer Institute (NCI)
Hierarchical Modeling Approaches for Geographical Boundary Analysis in Cancer Studies

Hierarchical models for Large Geostatistical Datasets with Applications to Forestry and Ecology
Principal Investigator: Sudipto Banerjee
Funding Agency: The National Science Foundation
Hierarchical models for Large Geostatistical Datasets with Applications to Forestry and Ecology

Hierarchical spatial process models for estimating
Principal Investigator: Sudipto Banerjee
Funding Agency: NIH NIGMS NATL INST OF GENERAL
Hierarchical spatial process models for estimating and p

Selected Publications:

Finley, A.O., Banerjee, S. and MacFarlane, D.W. (2011). A hierarchical model for predicting forest variables over large heterogeneous domains. Journal of the American Statistical Association 106, 31-48. pdf.

Banerjee, S., Finley, A.O., Waldmann, P. and Ericcson, T. (2010). Hierarchical spatial process models for multiple traits in large genetic trials. Journal of the American Statistical Association, 105, 506-521. pdf.

Ma, H., Carlin, B.P. and Banerjee, S. (2010). Hierarchical joint site-edge methods for medicare hospice service region boundary analysis. Biometrics, 66, 355-364. pdf.

Zhang, Y., Hodges, J.S. and Banerjee, S. (2009). Smoothed ANOVA with spatial effects as a competitor to MCAR in multivariate spatial smoothing. Annals of Applied Statistics 3, 1805-1830. pdf. Supplementary File.

Finley, A.O., Banerjee, S. and McRoberts, R.E. (2009). Hierarchical spatial models for predicting tree species assemblages across large domains. Annals of Applied Statistics, 3, 1052-1079. pdf.

Zhang, Y., Banerjee, S., Yang, R., Lungu, C. and Ramachandran, G. (2009). Bayesian modeling of air flow and exposure using two-zone models. Annals of Occupational Hygiene 53, 409-424. pdf.

Technical Reports:

  • rr2012-008.pdf Rajarshi Guhaniyogi, Andrew O. Finley, Sudipto Banerjee and Rich KobeModeling complex spatial dependencies: low-rank spatially-varying cross-covariances with application to soil nutrient data Research Report 2012-8, Division of Biostatistics, University of Minnesota, 2012 Journal of the American Statistical Association (in review).
  • rr2011-044.pdf Li, P., Banerjee, S., McBean, A.M. and Carlin, B.P.Bayesian areal wombling using false discovery rates. Research Report 2011-44, Division of Biostatistics, University of Minnesota, 2011 Statistics and Its Interface
  • rr2011-045.pdf Finley, A.O., Banerjee, S. and Basso, B. Improving crop model inference through Bayesian meld-ing with spatially-varying parameters. Research Report 2011-45, Division of Biostatistics, University of Minnesota, 2011 Journal of Agricultural, Biological and Environmental Statistics
  • rr2011-046.pdf Finley, A.O., Banerjee, S. and Gelfand, A.E. Bayesian dynamic modeling for large space-time datasets using Gaussian predictive processes. Research Report 2011-46, Division of Biostatistics, University of Minnesota, 2011 Journal of Geographical Information Systems.
  • rr2011-047.pdf Banerjee, S. and Fuentes, MBayesian modeling for large spatial datasets. Research Report 2011-47, Division of Biostatistics, University of Minnesota, 2011 WIREs Computational Statistics.
  • rr2011-048.pdf Ren, Q., Banerjee, S., Finley, A.O. and Hodges, J.S.Variational Bayesian methods for spatial data analysis.Research Report 2011-48, Division of Biostatistics, University of Minnesota, 2011Computational Statistics and Data Analysis.
  • rr2011-049.pdf Gelfand, A.E., Banerjee, S. and Finley, A.O.Spatial design for knot selection in knot-based dimension reduction models.Research Report 2011-49, Division of Biostatistics, University of Minnesota, 2011 In Spatio-temporal Design: Advances in Efficient Data Acquisition, eds. J.Mateu and W. Muller. Chichester, UK: John Wiley.
  • rr2011-050.pdf Quick, H., Banerjee, S. and Carlin, B.P.Modeling temporal gradients in regionally aggregated California asthma hospitalization dataResearch Report 2011-50, Division of Biostatistics, University of Minnesota, 2011Annals of Applied Statistics (in review).
  • rr2011-051.pdf Ren, Q. and Banerjee, S.Flexible predictive process spatial factor models for misaligned data sets.Research Report 2011-51, Division of Biostatistics, University of Minnesota, 2011Biometrics (in review).
  • rr2011-052.pdf Logan, P.W., Ramachandran G., Mulhausen, J.R., Banerjee, S. and Hewett, P.Desktop study of occupational exposure judgments: Does education and experience influence accuracy?Research Report 2011-52, Division of Biostatistics, University of Minnesota, 2011 Journal of Occupational and Environmental Hygiene (in press).
  • rr2011-053.pdf Vadali, M., Ramachandran, G. and Banerjee, S.Effect of training, education, professional experience and need for cognition on decision making in occupational exposure assessment.Research Report 2011-53, Division of Biostatistics, University of Minnesota, 2011QAnnals of Occupational Hygiene (To appear in 2012).
  • rr2010-017.pdf Monteiro, J., Banerjee, S. and Ramachandran, G.B2Z: An R package for Bayesian two-zone models.Research Report 2010-17, Division of Biostatistics, University of Minnesota, 2010Accepted in Journal of Statistical Software.
  • rr2010-016.pdf Eidsvik, J., Finley, A.O., Banerjee, S. and Rue, H.Approximate Bayesian inference for large spatial datasets using predictive process models.Research Report 2010-16, Division of Biostatistics, University of Minnesota, 2010Journal of Computational and Graphical Statistics (in press).
  • rr2010-015.pdf Finley, A.O., Banerjee, S. and MacFarlane, D.W.A hierarchical model for predicting forest variables over large heterogeneous domains.Research Report 2010-15, Division of Biostatistics, University of Minnesota, 2010Journal of the American Statistical Association (appeared in 2011).
  • rr2010-014.pdf Li, P., Banerjee, S., Hanson, T.A. and McBean, A.M.Nonparametric hierarchical modeling for detecting boundaries in areally referenced spatial datasets.Research Report 2010-14, Division of Biostatistics, University of Minnesota, 2010Biometrics (under revision)
  • rr2010-012.pdf Banerjee, S., Finley, A.O., Waldmann, P. and Ericsson, T.Hierarchical spatial process models for multiple traits in large genetic trials.Research Report 2010-12, Division of Biostatistics, University of Minnesota, 2010Journal of the American Statistical Association, 105, 506-521.
  • rr2010-011.pdf Sinha, D.K., Gu, Y. and Banerjee, S.Analysis of cure rate survival data under a proportional odds model.Research Report 2010-11, Division of Biostatistics, University of Minnesota, 2010Lifetime Data Analysis (in press).
  • rr2010-009.pdf Gelfand, A.E. and Banerjee, S.Multivariate spatial process models.Research Report 2010-9, Division of Biostatistics, University of Minnesota, 2010 In Handbook of Spatial Statistics, eds. P. Diggle, M. Fuentes, A.E. Gelfand, and P. Guttorp, Boca Ra
  • rr2010-008.pdf Banerjee, S.Spatial gradients and wombling.Research Report 2010-8, Division of Biostatistics, University of Minnesota, 2010Handbook of Spatial Statistics, eds. P. Diggle, M. Fuentes, A.E. Gelfand, and P. Guttorp, Boca Raton
  • rr2008-04.pdf Finley, A.O., Sang, H., Banerjee, S. and Gelfand A.E.Improving the Performance of Predictive Process Modeling for Large Spatial Datasets.Research Report 2008-4, Division of Biostatistics, University of Minnesota, 2008Submitted to Computational Statistics and Data Analysis
  • rr2007-030.pdf Andrew O. Finley, Sudipto Banerjee, Patrick Waldman and Tore Ericcson.Hierarchical spatial modeling of additive and dominance genetic variance for large spatial trial datasetsResearch Report 2007-30, Division of Biostatistics, University of Minnesota, 2007Biometrics, (revision submitted).
  • rr2007-016.pdf Sudipto Banerjee, Alan E. Gelfand, Andrew O. Finley and Huiyan Sang.Gaussian predictive process models for large spatial datasets.Research Report 2007-16, Division of Biostatistics, University of Minnesota, 2007Journal of the Royal Statistical Society Series B, (revision submitted).
  • rr2007-015.pdf Andrew O. Finley, Sudipto Banerjee, Alan R. Ek and Ronald McRoberts. Bayesian multivariate process modelling for predicting forest attributes.Research Report 2007-15, Division of Biostatistics, University of Minnesota, 2007Journal of Agricultural, Biological and Environmental Statistics, (to appear).
  • rr2007-014.pdf Sudipto Banerjee and Andrew O. Finley Bayesian multi-resolution modelling for spatially replicated datasets with application to forest biomass data. Research Report 2007-14, Division of Biostatistics, University of Minnesota, 2007Journal of Statistical Planning and Inference, (to appear).
  • rr2007-013.pdf Ulysses Diva, Sudipto Banerjee and Dipak K. DeyModelling spatially correlated survival data for individuals with multiple cancers. Research Report 2007-13, Division of Biostatistics, University of Minnesota, 2007Statistical Modelling, 7, 191-213.
  • rr2005-007.pdf Banerjee, S. and Johnson, G.A. Coregionalized Single-and Multi-Resolution Spatially-Varying Growth Curve Modelling with Application to Weed Growth. Research Report 2005-7, Division of Biostatistics, University of Minnesota, 2005 Submitted to Biometrics
  • rr2005-006.pdf Cooner, F., Banerjee, S., Carlin, B.P. and Sinha, D. Flexible Cure Rate Modelling Under Latent Activation Schemes Research Report 2005-6, Division of Biostatistics, University of Minnesota, 2005 Submitted to J. Amer. Statist. Assoc.
  • rr2005-001.pdf.gz Jin, X., Banerjee, S., and Carlin, B.P.Order-free coregionalized lattice models with application to multiple disease mapping Research Report 2005-1, Division of Biostatistics, University of Minnesota, 2005 Submitted to J. Amer. Statist. Assoc.
  • rr2003-009.pdf Banerjee, S. Essential Geodesics for the Spatial Statistician Research Report 2003-9, Division of Biostatistics, University of Minnesota, 2003
  • rr2003-008.pdf Banerjee, S., Johnson, G.A., Schneider, N. and Durgan, B.R. Modelling Replicated Weed Growth Using Spatially Varying Growth Curves Research Report 2003-8, Division of Biostatistics, University of Minnesota, 2003 To appear in Environmental and Ecological Statistics.
  • rr2003-006.pdf Banerjee, S., Gamerman, D. and Gelfand, A.E. Spatial Process Modelling for Univariate and Multivariate Dynamic Spatial Data Research Report 2003-6, Division of Biostatistics, University of Minnesota, 2003 A modified (shortened) version to appear in Environmetrics.
  • rr2002-022.ps.gz Banerjee, S. and Carlin, B.P. Parametric spatial cure rate models for right and interval-censored time-to-relapse data. Research Report 2002-22, Division of Biostatistics, University of Minnesota, 2002 Submitted to Biometrics
  • rr2001-029.ps.gz Banerjee, S. and Carlin, B.P. Semiparametric spatio-temporal frailty modeling Research Report 2001-29, Division of Biostatistics, University of Minnesota, 2001 Submitted to Environmetrics.
  • rr2001-004.ps.gz Banerjee, S., Wall, M.M., and Carlin, B.P. Frailty Modeling for Spatially Correlated Survival Data, with Application to Infant Mortality in Minnesota Research Report 2001-4, Division of Biostatistics, University of Minnesota, 2001 To appear Biostatistics.