Abstract Number:
3508
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Wenlong Gong (1), Brian Reich (2)
Institutions:
(1) N/A, N/A, (2) North Carolina State University, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
Ambient air pollution measurements from regulatory monitoring networks are routinely used to support epidemiologic studies and environmental policy decision making. However, regulatory monitors are spatially sparse and preferentially located in areas with large populations. Numerical model output can be leveraged into the inference and prediction of air pollution data combining with measurements from monitors. Nonstationary covariance functions allow the model to adapt to spatial surfaces whose variability changes with location like air pollution data. In the paper, we employ localized covariance parameters learned from the numerical output model to knit together into a global nonstationary covariance, and use this nonstationary covariance in a fully Bayesian model in which the unknown spatial process has a Gaussian process prior distribution. We model the nonstationary structure with greatly reduced number of parameters to make it computationally feasible.
Keywords:
spatial model|Bayesian model| non-stationary model|air pollution|environment|
Sponsors:
Section on Statistics and the Environment
Tracks:
Spatio-temporal statistics
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