Books

Banerjee S., Carlin B.P., and Gelfand A.E. (2014). Hierarchical Modeling and Analysis for Spatial Data, Second Edition. Chapman & Hall/CRC Monographs on Statistics & Applied Probability.

Banerjee S. and Roy, A. (2014). Linear Algebra and Matrix Analysis for Statistics. Chapman & Hall/CRC Texts in Statistical Science.

Lawson, A., Banerjee S., Haining, P.R. and Ugarte, L. (2014). Handbook of Spatial Epidemiology. Chapman & Hall/CRC Handbooks of Modern Statistical Methods.

National Academies Reports

National Research Council. 2015. Affordability of National Flood Insurance Program Premiums: Report 1. Washington, DC: The National Academies Press. Published 2015; 166 pages. ISBN 978-0-309-37125-4.

National Academies of Sciences, Engineering, and Medicine 2016. Affordability of National Flood Insurance Program Premiums: Report 2. Washington, DC: The National Academies Press.

Research Articles

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.

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.

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.

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.

Banerjee, S. (2004). Revisiting spherical trigonometry with orthogonal projectors. The Mathematical Association of America's College Mathematics Journal. 35, 375-381.

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.

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.

Gelfand, A.E., Banerjee, S. and Gamerman, D. (2005). Spatial Process Modelling for Univariate and Multivariate Dynamic Spatial Data, Environmetrics, 16, 465-479.

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.

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.

Banerjee, S. and Johnson, G.A. (2006). On coregionalized models for spatially replicated experiments in weed proliferation studies. In Bayesian Statistics and its Applications, eds. S.K. Upadhyay, U. Singh and D.K. Dey. New Delhi: Anamaya Publishers.

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.

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.

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.

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.

Finley, A.O. and S. Banerjee. (2008) Bayesian spatial regression for multi-source mapping. Encyclopedia of Geographic Information Systems. Springer--Verlag, New York.

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.

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.

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.

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.

Lawson, A.B. and Banerjee, S. (2009). Bayesian spatial analysis. In The SAGE Handbook of Spatial Analysis, eds. A.S. Fotheringham and P.A. Rogerson, London, UK: SAGE Publications Ltd.

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.

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.

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.

Banerjee, S. and Gelfand, A.E. (2010). Modelling spatial gradients on response surfaces. In Frontiers of Statistical Decision Making and Bayesian Analysis, eds. M.H. Chen, D.K. Dey, P. Mueller, D. Sun and K. Ye, New York: Springer.

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.

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.

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, 32, 515–537.

Sinha, D.K., Gu, Y. and Banerjee, S. (2010). Analysis of cure rate survival data under a proportional odds model. Lifetime Data Analysis, 17, 123-134.

Li, P., Banerjee, S. and McBean, A.M. (2010). Mining edge effects in areally-referenced spatial data: A fast Bayesian model choice approach. Geoinformatica, 15, 435-454.

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, 106, 31-48.

Wang, X., Dey, D.K. and Banerjee, S. (2010). Non-Gaussian hierarchical generalized linear geostatistical model selection. In Frontiers of Statistical Decision Making and Bayesian Analysis, eds. M.H. Chen, D.K. Dey, P. Mueller, D. Sun and K. Ye, New York: Springer.

Monteiro, J., Banerjee, S. and Ramachandran, G. (2011). B2Z: An Rpackage for Bayesian two-zone models. Journal of Statistical Software, 43, Issue 2, 1-23.

Guhaniyogi, R., Finley, A.O., Banerjee, S. and Gelfand, A.E. (2011). Adaptive Gaussian predictive process models for large spatial datasets. Environmetrics, 22, 997-1007.

Ren, Q., Banerjee, S., Finley, A.O. and Hodges, J.S. (2011). Variational Bayesian methods for spatial data analysis. Computational Statistics and Data Analysis, 55, 3197-3217.

Finley, A.O., Banerjee, S. and Basso, B. (2011). Improving crop model inference through Bayesian melding with spatially-varying parameters. Journal of Agricultural, Biological and Environmental Statistics, 16, 453-474.

Eidsvik, J., Finley, A.O., Banerjee, S. and Rue, H. (2012). Approximate Bayesian inference for large spatial datasets using predictive process models. Computational Statistics and Data Analysis, 56, 1362-1380.

Banerjee, S. and Fuentes, M. (2012). Bayesian modeling for large spatial datasets. WIREs Computational Statistics, 4, 59-66.

Vadali, M., Ramachandran, G., Mulhausen, J.R. and Banerjee, S. (2012). Effect of training on exposure judgment accuracy and agreement among hygienists. Journal of Occupational and Environmental Hygiene, 9, 242-256.

Finley, A.O., Banerjee, S. and Gelfand, A.E. (2012). Bayesian dynamic modeling for large space-time datasets using Gaussian predictive processes. Journal of Geographical Information Systems, 14, 29-47.

Finley, A.O. and Banerjee, S. (2012). Point-referenced spatial modeling. In The SAGE Handbook of Multilevel Modeling, eds. Marc A. Scott, Jeffrey S. Simonoff, and Brian D. Marx, pp.559–580. Thousand Oaks, CA: Sage Publications.

Vadali, M., Ramachandran, G. and Banerjee, S. (2012). Effect of training, education, professional experience and need for cognition on decision making in occupational exposure assessment. Annals of Occupational Hygiene, 56, 292-304.

Gelfand, A.E., Banerjee, S. and Finley, A.O. (2012). Spatial design for knot selection in knot-based dimension reduction models. In Spatio-temporal Design: Advances in Efficient Data Acquisition, eds. J. Mateu and W. Muller. Chichester, UK: John Wiley, pp.142–169.

Li, P., Banerjee, S., McBean, A.M. and Carlin, B.P. (2012). Bayesian areal wombling using false discovery rates. Statistics and Its Interface, 5, 149-158.

Delamater, P.L., Finley, A.O. and Banerjee, S. (2012). An analysis of asthma hospitalizations, air pollution, and weather conditions in Los Angeles County, California. Science of the Total Environment, 425, 110-118.

Finley, A.O., Banerjee, S., Cook, B.D, and Bradford, J.B. (2013) Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets. International Journal of Applied Earth Observation and Geoinformation, 22, 147-160.

Quick, H., Banerjee, S. and Carlin, B.P. (2013). Modeling temporal gradients in regionally aggregated California asthma hospitalization data. Annals of Applied Statistics, 7, 154-176.

Ren, Q. and Banerjee, S. (2013). Hierarchical factor models for large spatially misaligned datasets: A low-rank predictive process approach. Biometrics, 69, 19–30.

Adgate, J.L., Banerjee, S., Wang, M., McKenzie, L.M., Hwang, J., Cho, S.J. and Ramachandran, G. (2013). Performance of carpet allergen samplers in controlled laboratory studies. Journal of Exposure Science and Environmental Epidemiology, 23, 385-391.

Guhaniyogi, R., Finley, A.O., Banerjee, S. and Kobe, R. (2013). Modeling complex spatial dependencies: low-rank spatially-varying cross-covariances with application to soil nutrient data. Journal of Agricultural Biological and Environmental Statistics, 18, 274-298.

Quick, H., Groth, C., Banerjee, S., Carlin, B.P., Stenzel, M.R., Stewart, P.A., Sandler, D.P., Engel, L.S. and Kwok, R.K. (2014). Exploration of the use of Bayesian modeling of gradients for censored spatiotemporal data from the Deepwater Horizon oil spill. Spatial Statistics, 9, 166-179.

Finley, A.O., Banerjee, S. and Cook, B.D. (2014). Bayesian hierarchical models for spatially misaligned multivariate environmental and ecological data. Methods in Ecology and Evolution, 5, 514-523.

Monteiro, J.V., Banerjee, S. and Ramachandran, G. (2014). Bayesian modeling for physical processes in industrial hygiene using misaligned workplace data, Technometrics, 56, 238-247.

Banerjee, S., Ramachandran, G., Vadali, M. and Sahmel, J. (2014). Bayesian hierarchical framework for occupational hygiene decision making. Annals of Occupational Hygiene, 58, 1079–1093.

Tran, H., Ramachandran, G., Banerjee, S., Stewart, P.A., Stenzel, M., Sandler, D., Kwok, R. and Engel, L. (2014). Comparison of methods for analyzing left-censored occupational exposure data. Annals of Occupational Hygiene, 58, 1126–1142.

Li, P., Banerjee, S., Hanson, T.A. and McBean, A.M. (2015). Nonparametric hierarchical modeling for detecting boundaries in areally referenced spatial datasets. Statistica Sinica, 25, 385-402.

Finley, A.O., Banerjee, S. and Gelfand, A.E. (2015). spBayes: for large univariate and multivariate point-referenced spatio-temporal data models. Journal of Statistical Software, Volume 64, Issue 13.

Finley, A.O., Banerjee, S., Babcock, C. and Cook, B.D. (2015). Dynamic spatial regression models for space-varying forest stand tables. Environmetrics, 25, 596--609.

Botella-Rocomara, P., Martinez-Beneito, M.A. and Banerjee, S. (2015). A unifying modeling framework for highly multivariate disease mapping. Statistics in Medicine, 34, 1548--1559.

Quick, H., Carlin, B.P. and Banerjee, S. (2015). Heteroscedastic CAR models for areally referenced temporal processes with an application to California asthma hospitalization data. Journal of the Royal Statistical Society, Series C, 64, 799--813.

Quick, H., Banerjee, S. and Carlin, B.P. (2015). Bayesian modeling and analysis for gradients in spatiotemporal processes. Biometrics, 71, 575--584.

Tran, H., Quick, H., Ramachandran, G., Banerjee, S., Stenzel, M., Sandler, D.P., Engel, L.S., Kwok, R.K., Blair, A. and Stewart, P.A. (2015). A comparison of the beta-substitution method and a Bayesian method for analyzing left-censored data. Annals of Occupational Hygiene, 60, 56--73.

Banerjee, S. (2016). Spatial data analysis. Annual Review of Public Health, 37, 47--60.

Foster, J.R., Finley, A.O., D'Amato, A.W., Bradford, J.B. and Banerjee, S. (2016) Predicting tree biomass growth in the temperate-boreal ecotone: is tree size, age, competition or climate response most important? Global Change Biology, 22, 2138--2151.

Banerjee, S. (2016). Spatial survival models. In Handbook of Spatial Epidemiology, eds. Andrew B. Lawson, Sudipto Banerjee, Robert P. Haining and María Dolores Ugarte. Boca Raton, FL: Taylor and Francis/CRC, pp. 303–315.

Banerjee, S. (2016). Multivariate spatial models. In Handbook of Spatial Epidemiology, eds. Andrew B. Lawson, Sudipto Banerjee, Robert P. Haining and María Dolores Ugarte. Boca Raton, FL: Taylor and Francis/CRC, pp. 375–397.

Datta, A., Banerjee, S., Finley, A.O., and Gelfand, A.E. (2016). Hierarchical nearest-neighbor Gaussian process models for large geostatistical datasets. Journal of the American Statistical Association, 111, 800--812.

Martinez-Beneito, M.A., Botella-Rocomara, P. and Banerjee, S. (2017). Towards a multi-dimensional approach to Bayesian disease mapping. Bayesian Analysis, 12, 239--259.

Datta, A., Banerjee, S., Finley, A.O., Hamm, N.A.S. and Schaap, M. (2016). Non-separable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with application to particulate matter analysis. Annals of Applied Statistics, 10, 1286--1316.

Datta, A., Banerjee, S., Finley, A.O. and Gelfand, A.E. (2016). On nearest-neighbor Gaussian process models for massive spatial data. WIREs Computational Statistics, 8, 162--171.

Gelfand, A.E. and Banerjee, S. (2017). Bayesian modeling and analysis of geostatistical data. Annual Review of Statistics and Its Application, 4, 245--266.

Groth, C., Banerjee, S., Ramachandran, G., Stenzel, M., Sandler, D., Blair, A., Engel, L., Kwok, R.K. and Stewart, P.A. (2017). Bivariate left-censored Bayesian model for predicting exposure: Preliminary analysis of worker exposure during the `Deepwater Horizon' oil spill. Annals of Work Exposures and Health, 61, 76--86.

Kwok, R., Engel, L.S., Miller, A.K., Blair, A., Curry, M.D., Jackson II, W.B., Stew-art, P.A., Stenzel, M.R., Birnbaum, L.S., Sandler, D.P., and the GuLF STUDY Research Team. (2017). The GuLF STUDY: A prospective study of persons involved in the Deepwater Horizon oil spill response and clean-up. Environmental Health Perspectives, 125, 570–578.

Finley, A.O., Banerjee, S., Zhou, Y. and Cook, B.D. (2017). Joint hierarchical models for sparsely sampled high-dimensional LiDAR and forest variables. Remote Sensing of Environment, 190, 149--161.

Stewart, P.A., Stenzel, M.R., Ramachandran, G., Banerjee, S., Huynh, T., Groth, C., Kwok, R.K., Blair, A., Engel, L.S. and Sandler, D.P. (2017). Development of a total hydrocarbon ordinal job-exposure matrix for workers corresponding to the Deepwater Horizon Disaster: The GuLF STUDY. Journal of Exposure Science and Environmental Epidemiology.

Banerjee, S. (2017). High-dimensional Bayesian geostatistics. Bayesian Analysis, 12, 583--614.

Abdalla, N., Banerjee, S., Ramachandran, G., Stenzel, M. and Stewart, P.A. (2018). Coastal Kriging: A Bayesian approach. Annals of Work Exposures and Health, 62, 818–827.

Bose, M., Hodges, J.S. and Banerjee, S. (2018). Toward a diagnostic toolkit for linear models with Gaussian-process distributed random effects. Biometrics, 74, 863–873.

Guhaniyogi, R. and Banerjee, S. (2018). Meta-Kriging: Scalable Bayesian modeling and inference for massive spatial datasets. Technometrics, 60, 430-444.

Guhaniyogi, R. and Banerjee, S. (2019). Multivariate spatial meta-kriging. Statistics and Probability Letters, 144, 3-8.

Groth, C.P., Banerjee, S., Ramachandran, G., Stenzel, M. and Stewart, P.A. (2019). Multivariate left-censored Bayesian modeling for predicting exposure using multiple chemical predicvetors. Environmetrics, 29:e2505.

Nemmers, T., Narayan, A. and Banerjee, S. (2019). Bayesian modeling and uncertainty quantification in descriptive social networks. Statistics and Its Interface, 12, 181-191.

Datta, A., Zou, H. and Banerjee, S. (2019). Bayesian high-dimensional regression for change-point analysis. Statistics and Its Interface, 12, 253-264.

Shirota, S., Gelfand, A.E. and Banerjee, S. (2019). Spatial joint species distribution modeling using Dirichlet processes. Statistica Sinica, 29, 1127–1154.

Taylor-Rodriguez, D., Finley, A.O., Datta, A., Babcock, C., Andersen, H.E., Cook, B.C., Morton, D.C. and Banerjee, S. (2019). Spatial factor models for high-dimensional and large spatial data: An application in forest variable mapping. Statistica Sinica, 29, 1155–1180.

Finley, A.O., Datta, A., Cook, B.C., Morton, D.C. Andersen, H.E. and Banerjee, S. (2019). Efficient algorithms for Bayesian nearest-neighbor Gaussian processes. Journal of Computational and Graphical Statistics, 29, 1155–1180.

Banerjee, S. (2019). Geostatistical modeling for environmental processes. In Handbook of Environmental and Ecological Statistics, eds. Alan E. Gelfand, Montserrat Fuentes, Jennifer A. Hoeting, Richard L. Smith. Boca Raton, FL: Taylor and Francis/CRC, pp. 375–397.

Zhang, Lu, Datta, A. and Banerjee, S. (2019). Practical Bayesian modeling and inference for massive spatial datasets on modest computing environments. Statistical Analysis and Data Mining: The ASA Data Science Journal, 29, 1155–1180.

Datta, A., Banerjee, S., Hodges, J.S. and Gao, L. (2019). Spatial disease mapping using directed acyclic graph auto-regressive (DAGAR) models. Bayesian Analysis, 14, 1221–1244.

Abdalla, N., Banerjee, S., Ramachandran, G. and Arnold, S. (2020). Bayesian state space modeling of physical processes in industrial hygiene. Technometrics, 62, 147–160.

Chan-Golston, A., Banerjee, S. and Handcock, M. (2020). Bayesian finite population modeling under spatial process settings. Environmetrics, 31, e2606.

Finley, A.O. and Banerjee, S. (2020). Bayesian spatially varying coefficient models in the spBayes R package. Environmental Modelling and Software, 125, 104608.

Tran, L.D., Rice, T.H., Ong, P.M., Banerjee, S., Liou, J. and Ponce, N.A. (2020). Impact of gentrification on adult mental health. Health Services Research, 55, 432--444.

Banerjee, S. (2020). Modeling massive spatial datasets using a conjugate Bayesian linear modeling framework. Spatial Statistics, 37, 100417.

Gao, L., Banerjee, S. and Datta, A. (2020). Spatial modeling for correlated cancers using bivariate directed graphs. Annals of Cancer Epidemiology, 4:8.

Rollinson, C.R., Finley, A.O., Alexander, M.R., Banerjee, S., Hamil, K-A.D., Koenig, L.E., Locke, H.D., Peterson, M., Tingley, M.W., Wheeler, K., Youngflesh, C. and Zipkin, E.F. (2021). Working across space and time: nonstationarity in ecological research and application. Frontiers in Ecology and the Environment, 19, 66-72.

Banerjee, S. (2020). Conjugate Bayesian regression models for massive geostatistical datasets. In Computational and Methodological Statistics and Biostatistics - Contemporary Essays in Advancement, eds. Andriette Bekker, Johan Ferreira and Din Chen. Basel, Switzerland: Springer Nature.

Wang, B., Banerjee, S. and Gupta, R. (in press). Bayesian spatial modeling for housing data in South Africa. Sankhya.

Huynh, T.B., Groth, C.P., Ramachandran, G., Banerjee, S., Stenzel, M., Quick, H., Blair, A., Engel L.S., Kwok, R.K., Sandler, D.P. and Stewart, P.A. (in press). Estimates of occupational inhalation exposures to six oil-related compounds on the four rig vessels responding to the Deepwater Horizon oil spill. Annals of Work Exposure and Health.

Pratt, G.C., Stenzel, M.R., Kwok, R.K., Groth C.P., Banerjee, S., Arnold, S.F., Engel, L.S., Sandler, D.P., and Stewart, P.A. (in press). Modeled air pollution from in situ burning and flaring of oil and gas released following the Deepwater Horizon disaster. Annals of Work Exposures and Health.

Huynh, T., Groth, C.P., Ramachandran, G., Banerjee, S., Stenzel, M.R., Blair, A., Sandler, D.P., Engel, L.S., Kwok R.K., and Stewart P.A. (in press). Estimates of inhalation exposures to oil-related components on the supporting vessels during the Deepwater Horizon oil spill. Annals of Work Exposures and Health.

Finley, A.O., Datta, A. and Banerjee, S. (in press). spNNGP: R Package for nearest neighbor Gaussian process models. Journal of Statistical Software.

Zhang, L., Banerjee, S. and Finley, A.O. (2021). High-dimensional multivariate geostatistics: A Bayesian Matrix-Normal approach. Environmetrics, 32:e2675.

Peruzzi, M., Banerjee, S. and Finley, A.O. (in press). Highly scalable Bayesian geostatistical modeling via meshed Gaussian processes on partitioned domains. Journal of the American Statistical Association.

Huynh, T., Groth, C.P., Ramachandran, G., Banerjee, S., Stenzel, M.R., Blair, A., Sandler, D.P., Engel, L.S., Kwok R.K., and Stewart P.A. (in press). Estimates of inhalation exposures among land workers during the Deepwater Horizon oil spill clean-up operations. Annals of Work Exposures and Health.

Li, Y., Nguyen, D.V., Banerjee, S., Rhee, C.M., Kalantar-Zadeh, K., Kurum, E. and Senturk, D. (in press). Multilevel modeling of spatially nested functional data: Spatiotemporal patterns of hospitalization rates in the US dialysis population. Statistics in Medicine.

Groth, C.P., Banerjee, S., Ramachandran, G., Kwok, R.K., Blair, A., Sandler, D.P., Engel, L.S., Stewart, P.A., and Stenzel, M.R. (in press). Methods for the analysis of 26 million VOC area observations during the Deepwater Horizon oil spill response and clean-up. Annals of Work Exposures and Health.

Zhang, L. and Banerjee, S. (in press). Spatial factor modeling: A Bayesian Matrix-Normal approach for misaligned data. Biometrics.

Tang, W., Zhang, L. and Banerjee, S. (2021). On identifiability and consistency of the nugget in Gaussian spatial process models. Journal of the Royal Statistical Society: Series B (Methodology), 83, 1044--1070.

Dey, D., Datta, A. and Banerjee, S. (in press). Graphical Gaussian process models for highly multivariate spatial data. Biometrika.