Sudipto Banerjee

Bayesian Inference for Spatially-Temporally Misaligned Data Using Predictive Stacking.

Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking.

Ambient exposure to fine particulate matter with oxidative potential affects oxidative stress biomarkers in pregnancy.

Bayesian Geostatistics Using Predictive Stacking.

Assessing spatial disparities: a Bayesian linear regression approach.

Fine particulate matter from burning oil and gas and associated neurological symptoms among Deepwater Horizon oil spill cleanup workers.

Investigating the Aliso Canyon gas blowout disaster and adverse birth outcomes: A quasiexperimental approach.

Associations between airborne crude oil chemicals and neurological symptoms among workers in the gulf long-term follow-up study.

Gridding and Parameter Expansion for Scalable Latent Gaussian Models of Spatial Multivariate Data.

Nonstationary Spatial Process Models with Spatially Varying Covariance Kernels.

Graph-constrained Analysis for Multivariate Functional Data.

A Bayesian Joint Model of Longitudinal Kidney Disease Progression, Recurrent Cardiovascular Events, and Terminal Event in Patients with Chronic Kidney Disease.

Toward spatio-temporal models to support national-scale forest carbon monitoring and reporting

Multivariate spatiotemporal functional principal component analysis for modeling hospitalization and mortality rates in the dialysis population.

Association between spill-related exposure to fine particulate matter and peripheral motor and sensory nerve function among oil spill response and cleanup workers following the Deepwater Horizon oil spill.

Fixed-Domain Asymptotics Under Vecchia's Approximation of Spatial Process Likelihoods.

Finite Population Survey Sampling: An Unapologetic Bayesian Perspective.

Models to Support Forest Inventory and Small Area Estimation Using Sparsely Sampled LiDAR: A Case Study Involving G-LiHT LiDAR in Tanana, Alaska

Discussion on "Bayesian meta-analysis of penetrance for cancer risk" by Thanthirige Lakshika M. Ruberu, Danielle Braun, Giovanni Parmigiani, and Swati Biswas.

Bayesian Modeling with Spatial Curvature Processes.

Multivariate varying coefficient spatiotemporal model.

Bayesian hierarchical modeling and inference for mechanistic systems in industrial hygiene.

Joint species distribution models with imperfect detection for high‐dimensional spatial data

Exposure to volatile hydrocarbons and neurologic function among oil spill workers up to 6 years after the Deepwater Horizon disaster

Exposure to volatile hydrocarbons and neurologic function among oil spill workers up to 6 years after the Deepwater Horizon disaster

Joint species distribution models with imperfect detection for high-dimensional spatial data.

Fine particulate matter and incident coronary heart disease events up to 10 years of follow-up among Deepwater Horizon oil spill workers.

Spatial Difference Boundary Detection for Multiple Outcomes Using Bayesian Disease Mapping.

BAYESIAN HIERARCHICAL MODELING AND ANALYSIS FOR ACTIGRAPH DATA FROM WEARABLE DEVICES.

Discussion of "Optimal test procedures for multiple hypotheses controlling the familywise expected loss" by Willi Maurer, Frank Bretz, and Xiaolei Xun.

Collaborative Research: Rates of Change and Boundary Assessment in Spatiotemporal Processes

Collaborative Research: Statistical Inference for High-dimensional Spatial-Temporal Process Models

Collaborative Research: High-Dimensional Spatial-Temporal Modeling and Inference for Large Multi-Source Environmental Monitoring Systems

III: Medium: Collaborative Research: Bayesian Modeling and Inference for Quantifying Terrestrial Ecosystem Functions

Collaborative Research: Hierarchical Sparsity-Inducing Gaussian Process Models for Bayesian Inference on Large Spatiotemporal Datasets

Hierarchical models for Large Geostatistical Datasets with Application

Hierarchical models for Large Geostatistical Datasets with Applications to Forestry and Ecology