Sudipto Banerjee

Latest

• Working across space and time: nonstationarity in ecological research and application
• Bayesian spatially varying coefficient models in the spBayes R package
• High-dimensional multivariate Geostatistics: A Bayesian Matrix-Normal Approach
• Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains
• R package for Nearest Neighbor Gaussian Process models
• Conjugate Nearest Neighbor Gaussian Process Models for Efficient Statistical Interpolation of Large Spatial Data
• Efficient algorithms for Bayesian nearest neighbor Gaussian processes
• Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping
• Joint hierarchical models for sparsely sampled high-dimensional LiDAR and forest variables
• Hierarchical nearest-neighbor Gaussian process models for large geostatistical datasets
• Nonseparable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with an application to particulate matter analysis
• On nearest-neighbor Gaussian process models for massive spatial data
• Predicting tree biomass growth in the temperate--boreal ecotone: Is tree size, age, competition, or climate response most important?
• spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models
• Bayesian hierarchical models for spatially misaligned data in R
• Dynamic spatial regression models for space-varying forest stand tables
• Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets
• Modeling complex spatial dependencies: Low-rank spatially varying cross-covariances with application to soil nutrient data
• An analysis of asthma hospitalizations, air pollution, and weather conditions in Los Angeles County, California
• Approximate Bayesian inference for large spatial datasets using predictive process models
• Bayesian dynamic modeling for large space-time datasets using Gaussian predictive processes
• Spatial design for knot selection in knot-based dimension reduction models
• A hierarchical model for quantifying forest variables over large heterogeneous landscapes with uncertain forest areas
• Adaptive Gaussian predictive process models for large spatial datasets
• Improving crop model inference through Bayesian melding with spatially varying parameters
• Variational Bayesian methods for spatial data analysis
• Hierarchical spatial process models for multiple traits in large genetic trials
• Hierarchical spatial modeling of additive and dominance genetic variance for large spatial trial datasets
• Hierarchical spatial models for predicting tree species assemblages across large domains
• Improving the performance of predictive process modeling for large datasets
• A Bayesian approach to multi-source forest area estimation
• Gaussian predictive process models for large spatial data sets
• Hierarchical multiresolution approaches for dense point-level breast cancer treatment data
• Bayesian multi-resolution modeling for spatially replicated data sets with application to forest biomass data
• spBayes: an R package for univariate and multivariate hierarchical point-referenced spatial models