6 papers
A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth
Saskia Comess, Howard H. Chang, Joshua L. Warren
Studies of the relationships between environmental exposures and adverse health outcomes often rely on a two-stage statistical modeling approach, where exposure is modeled/predicte…
Critical Window Variable Selection for Mixtures: Estimating the Impact of Multiple Air Pollutants on Stillbirth
Joshua L. Warren, Howard H. Chang, Lauren K. Warren +3
Understanding the role of time-varying pollution mixtures on human health is critical as people are simultaneously exposed to multiple pollutants during their lives. For vulnerable…
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…
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…
A Bayesian Downscaler Model to Estimate Daily PM2.5 levels in the Continental US
Yikai Wang, Xuefei Hu, Howard Chang +3
There has been growing interest in extending the coverage of ground PM2.5 monitoring networks based on satellite remote sensing data. With broad spatial and temporal coverage, sate…
Weighted SAMGSR: combining significance analysis of microarray-gene set reduction algorithm with pathway topology-based weights to select relevant genes
Suyan Tian, Howard H. Chang, Chi Wang
Introduction It has been demonstrated that a pathway-based feature selection method which incorporates biological information within pathways into the process of feature selection…