has been elected as Fellow of the International Society for Baysian Analysis (ISBA).
| Departments | Type of Faculty |
|---|---|
| Biostatistics | Full Time |
Statistical modeling and analysis of geographically referenced datasets, Bayesian statistics (theory and methods) and hierarchical modelling, statistical computing and related software development.
Honors and Awards:
Finley, A.O., Datta, A., Cook, B.C., Morton, D.C. Andersen, H.E. and Banerjee, S. (in press). Efficient algorithms for Bayesian nearest-neighbor Gaussian processes. Journal of Computational and Graphical Statistics. arxiv and DOI
Guhaniyogi, R. and Banerjee, S. (in press). Meta-Kriging: Scalable Bayesian modeling and inference for massive spatial datasets. Technometrics. DOI
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. arxiv and DOI
Banerjee, S. (2017). High-dimensional Bayesian geostatistics. Bayesian Analysis, 12, 583--614. arxiv andDOI
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. arxiv and DOI
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. arxiv and DOI
Quick, H., Banerjee, S. and Carlin, B.P. (2015). Bayesian modeling and analysis for gradients in spatiotemporal processes. Biometrics, 71, 575--584. pdf (full text) and supplementary material
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.
Ren, Q. and Banerjee, S. (2013). Hierarchical factor models for large spatially misaligned datasets: A low-rank predictive process approach. Biometrics, 69, 19-30.
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.
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.
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.
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.
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.
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.
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.
has been elected as Fellow of the International Society for Baysian Analysis (ISBA).
and collaborators received the American Statistical Association’s Outstanding Statistical Application Award for 2017.
was selected as a 2015 Institute of Mathematical Statistics Fellow and received the 2016 Distinguished Achievement Medal from the American Statistical Association Section on Statistics and the Environment.
