6 citations · 16 across the 7 of their papers we have counts for
7 papers
Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants
Gang Xu, Xin Zhou, Molin Wang +7
One way to quantify exposure to air pollution and its constituents in epidemiologic studies is to use an individual's nearest monitor. This strategy results in potential inaccuracy…
Variable importance measure for spatial machine learning models with application to air pollution exposure prediction
Si Cheng, Magali N. Blanco, Lianne Sheppard +2
Exposure assessment is fundamental to air pollution cohort studies. The objective is to predict air pollution exposures for study subjects at locations without data in order to opt…
Random Spatial Forests
Travis Hee Wai, Michael T. Young, Adam A. Szpiro
We introduce random spatial forests, a method of bagging regression trees allowing for spatial correlation. Our main contribution is the development of a computationally efficient…
Spatial Matrix Completion for Spatially-Misaligned and High-Dimensional Air Pollution Data
Phuong T. Vu, Adam A. Szpiro, Noah Simon
In health-pollution cohort studies, accurate predictions of pollutant concentrations at new locations are needed, since the locations of fixed monitoring sites and study participan…
Probabilistic Predictive Principal Component Analysis for Spatially-Misaligned and High-Dimensional Air Pollution Data with Missing Observations
Phuong T. Vu, Timothy V. Larson, Adam A. Szpiro
Accurate predictions of pollutant concentrations at new locations are often of interest in air pollution studies on fine particulate matters (PM), in which data is usually…
National PM2.5 and NO2 Exposure Models for China Based on Land Use Regression, Satellite Measurements, and Universal Kriging
Hao Xu, Matthew J. Bechle, Meng Wang +4
Outdoor air pollution is a major killer worldwide and the fourth largest contributor to the burden of disease in China. China is the most populous country in the world and also has…