99 citations · 233 across the 15 of their papers we have counts for
17 papers
A Spectral Confounder Adjustment for Spatial Regression with Multiple Exposures and Outcomes
Shih-Ni Prim, Yawen Guan, Shu Yang +4
Characterizing social vulnerability is fundamental to disaster response planning. Numerous vulnerability indicators have been developed, but they are typically not validated for th…
An Improved Doubly Robust Estimator Using Partially Recovered Unmeasured Spatial Confounder
Sayli Pokal, Yawen Guan, Honglang Wang +1
Studies in environmental and epidemiological sciences are often spatially varying and observational in nature with the aim of establishing cause and effect relationships. One of th…
A spectral adjustment for spatial confounding
Yawen Guan, Garritt L. Page, Brian J Reich +2
Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. In this paper, we derive necessa…
Kryging: Geostatistical analysis of large-scale datasets using Krylov subspace methods
Suman Majumder, Yawen Guan, Brian J. Reich +1
Analyzing massive spatial datasets using Gaussian process model poses computational challenges. This is a problem prevailing heavily in applications such as environmental modeling,…
A review of spatial causal inference methods for environmental and epidemiological applications
Brian J Reich, Shu Yang, Yawen Guan +3
The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Sp…
Statistical downscaling with spatial misalignment: Application to wildland fire PM concentration forecasting
Suman Majumder, Yawen Guan, Brian J. Reich +2
Fine particulate matter, PM, has been documented to have adverse health effects and wildland fires are a major contributor to PM air pollution in the US. Forecaster…
Multivariate spectral downscaling for PM2.5 species
Yawen Guan, Brian J Reich, James A Mulholland +1
Fine particulate matter (PM2.5) is a mixture of air pollutants that has adverse effects on human health. Understanding the health effects of PM2.5 mixture and its individual specie…
Fast expectation-maximization algorithms for spatial generalized linear mixed models
Yawen Guan, Murali Haran
Spatial generalized linear mixed models (SGLMMs) are popular and flexible models for non-Gaussian spatial data. They are useful for spatial interpolations as well as for fitting re…
Probabilistic Projections of Baseline 21st Century CO Emissions Using a Simple Calibrated Integrated Assessment Model
Vivek Srikrishnan, Yawen Guan, Richard S. J. Tol +1
Probabilistic projections of baseline (with no additional mitigation policies) future carbon emissions are important for sound climate risk assessments. Deep uncertainty surrounds…
Ice Model Calibration Using Semi-continuous Spatial Data
Won Chang, Bledar A. Konomi, Georgios Karagiannis +2
Rapid changes in Earth's cryosphere caused by human activity can lead to significant environmental impacts. Computer models provide a useful tool for understanding the behavior and…
A comparison of statistical and machine learning methods for creating national daily maps of ambient PM concentration
Veronica J. Berrocal, Yawen Guan, Amanda Muyskens +4
A typical problem in air pollution epidemiology is exposure assessment for individuals for which health data are available. Due to the sparsity of monitoring sites and the limited…
Nearest-Neighbor Neural Networks for Geostatistics
Haoyu Wang, Yawen Guan, Brian J Reich
Kriging is the predominant method used for spatial prediction, but relies on the assumption that predictions are linear combinations of the observations. Kriging often also relies…
Fine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles
Yawen Guan, Margaret Johnson, Matthias Katzfuss +4
People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. In order to make informed decisions on their…
MIMIX: a Bayesian Mixed-Effects Model for Microbiome Data from Designed Experiments
Neal S. Grantham, Brian J. Reich, Elizabeth T. Borer +1
Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these d…
Inferring Ice Thickness from a Glacier Dynamics Model and Multiple Surface Datasets
Yawen Guan, Murali Haran, David Pollard
The future behavior of the West Antarctic Ice Sheet (WAIS) may have a major impact on future climate. For instance, ice sheet melt may contribute significantly to global sea level…
Assessing the Impact of Retreat Mechanisms in a Simple Antarctic Ice Sheet Model Using Bayesian Calibration
Kelsey L. Ruckert, Gary Shaffer, David Pollard +4
The response of the Antarctic ice sheet (AIS) to changing climate forcings is an important driver of sea-level changes. Anthropogenic climate change may drive a sizeable AIS tippin…
A Computationally Efficient Projection-Based Approach for Spatial Generalized Linear Mixed Models
Yawen Guan, Murali Haran
Inference for spatial generalized linear mixed models (SGLMMs) for high-dimensional non-Gaussian spatial data is computationally intensive. The computational challenge is due to th…