Arranged chronologically.

• Banerjee S., Gelfand, A.E. and Polasek, W. (2000). Geostatistical modelling of spatial interaction data with application to postal service performance. Journal of Statistical Planning and Inference 90, 87-105.
• Banerjee, S. and Gelfand, A.E. (2002). Prediction, interpolation and regression for spatially misaligned datasets. Sankhya Series A 64, 227-245. pdf.
• Banerjee, S. and Carlin, B.P. (2002). Spatial semi-parametric proportional hazards models for analyzing infant mortality rates in Minnesota counties. In Case Studies in Bayesian Statistics Volume VI, eds. C. Gatsonis et al. New York: Springer.
• Banerjee, S. and Gelfand, A.E. (2003). On smoothness properties of spatial processes. Journal of Multivariate Analysis, 84, 85-100.
• Banerjee, S., Wall, M. and Carlin, B.P. (2003). Frailty modelling for spatially correlated survival data with application to infant mortality in Minnesota. Biostatistics 4123-142. pdf.
• Carlin, B.P. and Banerjee, S. (2003). Hierarchical multivariate CAR models for spatially correlated survival data. In Bayesian Statistics 7. Oxford: Oxford University Press, 45-64.
• Banerjee, S. and Carlin, B.P. (2003). Semiparametric spatiotemporal frailty modelling. Environmetrics 14, 523-535. pdf.
• Gelfand, A.E., Kim, H.K., Sirmans, C.F. and Banerjee, S. (2003). Spatial modelling with spatially varying coefficient processes. Journal of the American Statistical Association 98, 387-396.
• Ramachandran, G, Banerjee, S. and Vincent, JH (2003). Expert judgment and occupational hygiene: Application to aerosol speciation in the nickel primary production industry. Annals of Occupational Hygiene 47, 461-475.
• Banerjee, S., Gelfand, A.E. and Sirmans, C.F (2003). Directional Rates of Change Under Spatial Process Models. Journal of the American Statistical Association 98, 946-954.
• Banerjee S., Gelfand A.E., Knight J.R., and Sirmans C.F. (2004). Spatial modelling of house prices using normalized distance-weighted sums of stationary processes. Journal of Business and Economic Statistics. 22 206-213.
• Banerjee, S. and Carlin, B.P. (2004). Parametric spatial cure rate models for interval-censored time-to-relapse data. Biometrics 60, 268-275. pdf.
• Banerjee, S. (2004). Revisiting spherical trigonometry with orthogonal projectors. The Mathematical Association of America's College Mathematics Journal. 35, 375-381. pdf.
• Banerjee, S., Johnson, G.A., Schneider, N. and Durgan, B.R. (2004). Modelling replicated weed growth using spatially varying growth curves. Environmental and Ecological Statistics, 12, 357-377. pdf.
• Gelfand A.E., Schmidt, A., Banerjee S. and Sirmans C.F. (2004). Nonstationary multivariate process modelling through spatially varying coregionalization. Test, 13, 263-312.
• Majumdar, A., Gelfand, A.E. and Banerjee, S. (2004). Spatiotemporal Change-point Modelling. Journal of Statistical Planning and Inference, 130, 149-166.
• Banerjee, S. and Dey, D.K. (2005). Semi-parametric proportional odds models for spatially correlated survival data. Lifetime Data Analysis, 11, 175-191.
• Banerjee, S. (2005). On geodetic distance computations in spatial modelling. Biometrics 61, 617-625. pdf.
• Gelfand, A.E., Banerjee, S. and Gamerman, D. (2005). Spatial Process Modelling for Univariate and Multivariate Dynamic Spatial Data, Environmetrics, 16, 465-479. pdf.
• Jin, X., Carlin B.P., and Banerjee, S. (2005). Generalized hierarchical multivariate CAR models for areal data. Biometrics, 61, 950-961.
• Majumdar A., Gelfand A.E., Banerjee S., Munneke H.J. and Sirmans C.F. (2006). Gradients in spatial response surfaces using spatial process modelling with an application to land value gradients. Journal of Business and Economic Statistics, 24, 77-90.
• Hewett, P., Logan, P., Mulhausen, J., Ramachandran, G. and Banerjee, S. (2006). Rating exposure control using Bayesian decision analysis. Journal of Occupational and Environmental Hygiene, 3, 568-581.
• Cooner, F., Banerjee, S. and McBean, A.M. (2006). Modelling geographically referenced survival data with a cure fraction. Statistical Methods in Medical Research, 15, 307-324.
• Banerjee, S. and Gelfand, A.E. (2006). Bayesian Wombling: Curvilinear gradient assessment under spatial process models. Journal of the American Statistical Association, 101, 1487-1501. pdf
• Banerjee, S. and Johnson, G.A. (2006). Coregionalized single- and multi-resolution spatially-varying growth curve modelling with application to weed growth. Biometrics, 61, 617-625. pdf.
• Jin, X., Banerjee, S. and Carlin, B.P. (2007). Order-free coregionalized lattice models with application to multiple disease mapping. Journal of the Royal Statistical Society Series B, 69, 817-838. pdf.
• Gelfand, A.E., Banerjee, S., Sirmans, C.F., Tu, Y. and Ong, S.E. (2007). Multilevel modelling using spatial processes: application to the Singapore housing market. Computational Statistics and Data Analysis 52 2650-2668.
• Diva, U., Banerjee, S. and Dey, D.K. (2007). Modelling spatially correlated survival data for individuals with multiple cancers. Statistical Modelling, 7, 191-213.
• Banerjee, S., Kauffman, R.J. and Wang, B. (2007). Modeling Internet firm survival using Bayesian dynamic models with time-varying coefficients. Electronic Commerce Research and its Applications, 6, 332-342.
• Cooner, F., Banerjee, S., Carlin, B.P. and Sinha, D. (2007). Flexible cure rate modelling under latent activation schemes. Journal of the American Statistical Association, 102, 560-572. pdf.
• Banerjee, S. and Finley, A.O. (2007). Bayesian multi-resolution modelling for spatially replicated datasets with application to forest biomass data. Journal of Statistical Planning and Inference, 137, 3193-3205.
• Lu, H., Reilly, C., Banerjee, S. and Carlin, B.P. (2007). Bayesian areal wombling via adjacency modelling. Environmental and Ecological Statistics, 14, 433-452.
• Diva, U., Dey, D.K. and Banerjee, S. (2008). Parametric models for spatially correlated survival data for individuals with multiple cancers. Statistics in Medicine, 27, 2127-2144. pdf.
• Finley, A.O., Banerjee, S., Ek, A.R. and McRoberts, R. (2008). Bayesian multivariate process modeling for predicting forest attributes. Journal of Agricultural, Biological and Environmental Statistics, 13, 1-24.
• Finley, A.O., Banerjee, S. and McRoberts, R.E. (2008). A Bayesian approach to multi-source forest area estimation. Environmental and Ecological Statistics, 15, 241-258.

• Banerjee, S., Gelfand, A.E., Finley, A.O. and Sang, H. (2008). Gaussian predictive process models for large spatial datasets. Journal of the Royal Statistical Society Series B, 70, 825-848. pdf.
• Finley, A.O., Banerjee, S., Waldmann, P. and Ericsonn, T. (2009). Hierarchical spatial modelling of additive and dominance genetic variance for large spatial trial datasets. Biometrics, 61, 441-451. pdf.
• Finley, A.O., Sang, H., Banerjee, S. and Gelfand, A.E. (2009). Improving the performance of predictive process modeling for large datasets. Computational Statistics and Data Analysis, 53, 2873-2884.
• Zhang, Y., Banerjee, S., Yang, R., Lungu, C. and Ramachandran, G. (2009). Bayesian modelling of air flow and exposure using two-zone models. Annals of Occupational Hygiene 53, 409-424. pdf.
• Latimer, A.M., Banerjee, S., Sang, H., Mosher Jr., E. and Silander, J.A. (2009). Hierarchical models for spatial analysis of large data sets: A case study on invasive plant species in the northeastern United States. Ecology Letters, 12, 144-54.
• Cooner, F.W., Yu, X., Banerjee, S., Grambsch, P.L. and McBean, A.M. (2009). Hierarchical dynamic time-to-event models for post-treatment preventive care data on breast cancer survivors. Statistical Modelling, 9, 119-135.
• Cho, S.J., Ramachandran, G., Banerjee, S., Ryan, A.D., Adgate, J.L. (2008). Seasonal variability of culturable fungal genera in the house dust of inner-city residences. Journal Of Occupational and Environmental Hygiene, 5, 780-789.
• Zimmerman, G., Gutierrez, R.J., Thogmartin, W.E. and Banerjee, S. (2009). Multiscale habitat selection by Ruffed Grouse at low population densities. The Condor, 111, 294-304.
• 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. Supplementary File.
• 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.
• Banerjee, S. (2010). Spatial gradients and wombling. In Handbook of Spatial Statistics, eds. P. Diggle, M. Fuentes, A.E. Gelfand, and P. Guttorp, Boca Raton, FL: Taylor and Francis.
• Gelfand, A.E. and Banerjee, S. (2010). Multivariate spatial process models. In Handbook of Spatial Statistics, eds. P. Diggle, M. Fuentes, A.E. Gelfand, and P. Guttorp, Boca Raton, FL: Taylor and Francis.
• Liang, S., Banerjee, S. and Carlin, B.P. (2010). Bayesian wombling for spatial point processes. Biometrics, 65, 1243-1253. 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.
• Thelemann, R. Johnson, G.A., Sheaffer, C., Banerjee, S., Cai, H. and Wyse, D. (2010). Biomass crop yield as a function of landscape position. Agronomy Journal, 102, 513-522.
• 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.
• Narayan, A., Purkayastha, B. and Banerjee, S. (2010). Constructing transnational and virtual ethnic identities: A study of the discourse and networks of ethnic student organisations in the US and the UK. Journal of Intercultural Studies (in press).
• Sinha, D.K., Gu, Y. and Banerjee, S. (2010). Analysis of cure rate survival data under a proportional odds model. Lifetime Data Analysis (in press).
• Li, P., Banerjee, S. and McBean, A.M. (2010). Mining edge effects in areally-referenced spatial data: A fast Bayesian model choice approach. Geoinformatica (in press).
• Finley, A.O., Banerjee, S. and MacFarlane, D.W. (2010). A hierarchical model for predicting forest variables over large heterogeneous domains. Journal of the American Statistical Association (in press). pdf.