Journal article

Variable selection for high dimensional Bayesian density estimation: application to human exposure simulation

Brian J Reich, Eric Kalendra, Curtis B Storlie, Howard D Bondell, Montserrat Fuentes

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS | WILEY | Published : 2012

Abstract

Numerous studies have linked ambient air pollution and adverse health outcomes. Many studies of this nature relate outdoor pollution levels measured at a few monitoring stations with health outcomes. Recently, computational methods have been developed to model the distribution of personal exposures, rather than ambient concentration, and then relate the exposure distribution to the health outcome. Although these methods show great promise, they are limited by the computational demands of the exposure model. We propose a method to alleviate these computational burdens with the eventual goal of implementing a national study of the health effects of air pollution exposure. Our approach is to de..

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University of Melbourne Researchers

Grants

Awarded by National Science Foundation


Awarded by Sandia National Laboratories


Awarded by Environmental Protection Agency


Awarded by National Institutes of Health


Funding Acknowledgements

The authors thank the National Science Foundation (Reich, DMS-0354189; Bondell, DMS-0705968; Fuentes, DMS-0706731 and DMS-0353029), Sandia National Laboratories (Storlie, Sandia University Research Program grant 22858), the Environmental Protection Agency (Fuentes, R833863) and National Institutes of Health (Fuentes, 5R01ES014843-02) for partial support of this work. The authors also thank John Langstaff of the US Environmental Protection Agency for his help with the APEX model and interpreting the results.