Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Spatial regression with an informatively missing covariate: Application to mapping fine particulate matter
The United States Environmental Protection Agency has established a large network of stations to monitor fine particulate matter of <2.5 µm (PM2.5) that is known to be harmful to human health. Unfortunately, the network has limited spatial coverage, and stations often only measure PM2.5 every few days. Satellite‐measured aerosol optical depth (AOD) is a low‐cost surrogate with greater spatiotemporal coverage, and spatial regression models have established that including AOD as a covariate improves the spatial interpolation of PM2.5. However, AOD is often missing, and our analysis reveals that the conditions that lead to missing AOD are also conducive to high AOD. Therefore, naïve interpolation that ignores informative missingness may lead to bias. We propose a joint hierarchical model for PM2.5 and AOD that accounts for informatively missing AOD. We conduct a simulation study of the effects of ignoring informative missingness in the covariate and evaluate the performance of the proposed model. We apply the method to map daily PM2.5 in the Southeastern United States. Our analysis reveals statistically significant informative missingness and relationships between PM2.5 and AOD in many seasons after accounting for meteorological and land‐use variables.
Spatial regression with an informatively missing covariate: Application to mapping fine particulate matter
The United States Environmental Protection Agency has established a large network of stations to monitor fine particulate matter of <2.5 µm (PM2.5) that is known to be harmful to human health. Unfortunately, the network has limited spatial coverage, and stations often only measure PM2.5 every few days. Satellite‐measured aerosol optical depth (AOD) is a low‐cost surrogate with greater spatiotemporal coverage, and spatial regression models have established that including AOD as a covariate improves the spatial interpolation of PM2.5. However, AOD is often missing, and our analysis reveals that the conditions that lead to missing AOD are also conducive to high AOD. Therefore, naïve interpolation that ignores informative missingness may lead to bias. We propose a joint hierarchical model for PM2.5 and AOD that accounts for informatively missing AOD. We conduct a simulation study of the effects of ignoring informative missingness in the covariate and evaluate the performance of the proposed model. We apply the method to map daily PM2.5 in the Southeastern United States. Our analysis reveals statistically significant informative missingness and relationships between PM2.5 and AOD in many seasons after accounting for meteorological and land‐use variables.
Spatial regression with an informatively missing covariate: Application to mapping fine particulate matter
Grantham, Neal S. (Autor:in) / Reich, Brian J. (Autor:in) / Liu, Yang (Autor:in) / Chang, Howard H. (Autor:in)
Environmetrics ; 29
01.06.2018
1 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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