99 citations · 233 across the 15 of their papers we have counts for
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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…