45 citations · 341 across the 31 of their papers we have counts for
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A Note on the comparison of Nearest Neighbor Gaussian Process (NNGP) based models
Lu Zhang, Sudipto Banerjee
This note is devoted to the comparison between two Nearest-neighbor Gaussian processes (NNGP) based models: the response NNGP model and the latent NNGP model. We exhibit that the c…
Coastline Kriging: A Bayesian Approach
Nada Abdalla, Sudipto Banerjee, Gurumurthy Ramachandran +2
Statistical interpolation of chemical concentrations at new locations is an important step in assessing a worker's exposure level. When measurements are available from coastlines,…
Bayesian State Space Modeling of Physical Processes in Industrial Hygiene
Nada Abdalla, Sudipto Banerjee, Gurumurthy Ramachandran +1
Exposure assessment models are deterministic models derived from physical-chemical laws. In real workplace settings, chemical concentration measurements can be noisy and indirectly…
Toward a diagnostic toolkit for linear models with Gaussian-process distributed random effects
Maitreyee Bose, James S. Hodges, Sudipto Banerjee
Gaussian processes (GPs) are widely used as distributions of random effects in linear mixed models, which are fit using the restricted likelihood or the closely-related Bayesian an…
Scalable Inference for Space-Time Gaussian Cox Processes
Shinichiro Shirota, Sudipto Banerjee
The log-Gaussian Cox process is a flexible and popular class of point pattern models for capturing spatial and space-time dependence for point patterns. Model fitting requires appr…
Practical Bayesian Modeling and Inference for Massive Spatial Datasets On Modest Computing Environments
Lu Zhang, Abhirup Datta, Sudipto Banerjee
With continued advances in Geographic Information Systems and related computational technologies, statisticians are often required to analyze very large spatial datasets. This has…