Bayesian Spatial Quantile Regression

Brian J. Reich; Montserrat Fuentes; David B. Dunson

doi:10.1198/jasa.2010.ap09237

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Journal article

Peer reviewed

Bayesian Spatial Quantile Regression

Brian J. Reich, Montserrat Fuentes and David B. Dunson

Journal of the American Statistical Association, Vol.106(493), pp.6-20

03/01/2011

DOI: 10.1198/jasa.2010.ap09237

PMCID: PMC3583387

PMID: 23459794

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https://www.ncbi.nlm.nih.gov/pmc/articles/3583387View

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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.

Climate Change

Ozone

Meteorology

Datasets

Cloud cover

Climate models

Regression analysis

Quantile regression

Spatial models

Modeling

Applications and Case Studies

Details

Title: Subtitle: Bayesian Spatial Quantile Regression
Creators: Brian J. Reich
Montserrat Fuentes - North Carolina State University
David B. Dunson
Resource Type: Journal article
Publication Details: Journal of the American Statistical Association, Vol.106(493), pp.6-20
Event: 0
DOI: 10.1198/jasa.2010.ap09237
PMID: 23459794
PMCID: PMC3583387
NLM abbreviation: J Am Stat Assoc
ISSN: 0162-1459
eISSN: 1537-274X
Publisher: American Statistical Association
Language: English
Date published: 03/01/2011
Academic Unit: Statistics and Actuarial Science; Biostatistics; Provost Office Administration
Record Identifier: 9983756766102771

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