5 citations · 17 across the 14 of their papers we have counts for
14 papers
Leveraging national forest inventory data to estimate forest carbon density status and trends for small areas
Elliot S. Shannon, Andrew O. Finley, Paul B. May +5
National forest inventory (NFI) data are often costly to collect, which inhibits efforts to estimate parameters of interest for small spatial, temporal, or biophysical domains. Tra…
Spatio-temporal areal models to support small area estimation: An application to national-scale forest carbon monitoring
Elliot S. Shannon, Andrew O. Finley, Paul B. May +4
National Forest Inventory (NFI) programs can provide vital information on the status, trend, and change in forest parameters. These programs are being increasingly asked to provide…
Bayesian Modeling of Incompatible Spatial Data: A Case Study Involving Post-Adrian Storm Forest Damage Assessment
Lu Zhang, Andrew O. Finley, Arne Nothdurft +1
Modeling incompatible spatial data, i.e., data with different spatial resolutions, is a pervasive challenge in remote sensing data analysis. Typical approaches to addressing this c…
Bayesian Hierarchical Modeling and Inference for Mechanistic Systems in Industrial Hygiene
Soumyakanti Pan, Darpan Das, Gurumurthy Ramachandran +1
A series of experiments in stationary and moving passenger rail cars were conducted to measure removal rates of particles in the size ranges of SARS-CoV-2 viral aerosols, and the a…
Finite Population Survey Sampling: An Unapologetic Bayesian Perspective
Sudipto Banerjee
This article attempts to offer some perspectives on Bayesian inference for finite population quantities when the units in the population are assumed to exhibit complex dependencies…
Bayesian Geostatistics Using Predictive Stacking
Lu Zhang, Wenpin Tang, Sudipto Banerjee
We develop Bayesian predictive stacking for geostatistical models, where the primary inferential objective is to provide inference on the latent spatial random field and conduct sp…
Bayesian modeling with spatial curvature processes
Aritra Halder, Sudipto Banerjee, Dipak K. Dey
Spatial process models are widely used for modeling point-referenced variables arising from diverse scientific domains. Analyzing the resulting random surface provides deeper insig…
Models to support forest inventory and small area estimation using sparsely sampled LiDAR: A case study involving G-LiHT LiDAR in Tanana, Alaska
Andrew O. Finley, Hans-Erik Andersen, Chad Babcock +3
A two-stage hierarchical Bayesian model is developed and implemented to estimate forest biomass density and total given sparsely sampled LiDAR and georeferenced forest inventory pl…
Graph-constrained Analysis for Multivariate Functional Data
Debangan Dey, Sudipto Banerjee, Martin Lindquist +1
Functional Gaussian graphical models (GGM) used for analyzing multivariate functional data customarily estimate an unknown graphical model representing the conditional relationship…
Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models
Sudipto Banerjee, Xiang Chen, Ian Frankenburg +1
We develop an approach for Bayesian learning of spatiotemporal dynamical mechanistic models. Such learning consists of statistical emulation of the mechanistic system that can effi…
Bayesian Models for Multivariate Difference Boundary Detection in Areal Data
Leiwen Gao, Sudipto Banerjee, Beate Ritz
Regional aggregates of health outcomes over delineated administrative units (e.g., states, counties, zip codes), or areal units, are widely used by epidemiologists to map mortality…
Fixed-Domain Asymptotics Under Vecchia's Approximation of Spatial Process Likelihoods
Lu Zhang, Wenpin Tang, Sudipto Banerjee
Statistical modeling for massive spatial data sets has generated a substantial literature on scalable spatial processes based upon Vecchia's approximation. Vecchia's approximation…
Bayesian Hierarchical Modeling and Analysis for Actigraph Data from Wearable Devices
Pierfrancesco Alaimo Di Loro, Marco Mingione, Jonah Lipsitt +3
The majority of Americans fail to achieve recommended levels of physical activity, which leads to numerous preventable health problems such as diabetes, hypertension, and heart dis…
spNNGP R package for Nearest Neighbor Gaussian Process models
Andrew O. Finley, Abhirup Datta, Sudipto Banerjee
This paper describes and illustrates functionality of the spNNGP R package. The package provides a suite of spatial regression models for Gaussian and non-Gaussian point-referenced…