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dc.contributor.advisor Gelfand, Alan E en_US
dc.contributor.author Reich, BJ
dc.contributor.author Fuentes, M
dc.contributor.author Dunson, DB
dc.coverage.spatial United States
dc.date.accessioned 2011-01-05T14:40:07Z
dc.date.issued 2011-03
dc.identifier http://www.ncbi.nlm.nih.gov/pubmed/23459794
dc.identifier.citation J Am Stat Assoc, 2011, 106 (493), pp. 6 - 20
dc.identifier.issn 0162-1459
dc.identifier.uri http://hdl.handle.net/10161/2981
dc.description Dissertation en_US
dc.description.abstract Tropospheric ozone is one of the six criteria pollutants regulated by the United States Environmental Protection Agency under the Clean Air Act and has been linked with several adverse health effects, including mortality. Due to the strong dependence on weather conditions, ozone may be sensitive to climate change and there is great interest in studying the potential effect of climate change on ozone, and how this change may affect public health. In this paper we develop a Bayesian spatial model to predict ozone under different meteorological conditions, and use this model to study spatial and temporal trends and to forecast ozone concentrations under different climate scenarios. We develop a spatial quantile regression model that does not assume normality and allows the covariates to affect the entire conditional distribution, rather than just the mean. The conditional distribution is allowed to vary from site-to-site and is smoothed with a spatial prior. For extremely large datasets our model is computationally infeasible, and we develop an approximate method. We apply the approximate version of our model to summer ozone from 1997-2005 in the Eastern U.S., and use deterministic climate models to project ozone under future climate conditions. Our analysis suggests that holding all other factors fixed, an increase in daily average temperature will lead to the largest increase in ozone in the Industrial Midwest and Northeast.
dc.format.extent 6 - 20
dc.language ENG
dc.relation.ispartof J Am Stat Assoc
dc.relation.isversionof 10.1198/jasa.2010.ap09237
dc.subject Climate change
dc.subject Ozone
dc.subject Semiparametric Bayesian methods
dc.subject Spatial data
dc.title Bayesian Spatial Quantile Regression.
dc.type Journal Article
dc.department Statistical Science en_US
pubs.author-url http://www.ncbi.nlm.nih.gov/pubmed/23459794
pubs.issue 493
pubs.organisational-group /Duke
pubs.organisational-group /Duke/Institutes and Provost's Academic Units
pubs.organisational-group /Duke/Institutes and Provost's Academic Units/University Institutes and Centers
pubs.organisational-group /Duke/Institutes and Provost's Academic Units/University Institutes and Centers/Duke Institute for Brain Sciences
pubs.organisational-group /Duke/Pratt School of Engineering
pubs.organisational-group /Duke/Pratt School of Engineering/Electrical and Computer Engineering
pubs.organisational-group /Duke/Trinity College of Arts & Sciences
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Mathematics
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Statistical Science
pubs.publication-status Published
pubs.volume 106

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