Multipollutant measurement error in air pollution epidemiology studies arising from predicting exposures with penalized regression splines-Reference-Cited by-同舟云学术

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Multipollutant measurement error in air pollution epidemiology studies arising from predicting exposures with penalized regression splines

Published:2016-03-01 Issue:5 Volume:65 Page:731-753
ISSN:0035-9254
Container-title:Journal of the Royal Statistical Society: Series C (Applied Statistics)
language:en
Short-container-title:J. R. Stat. Soc. C

Author:

Bergen Silas¹,Sheppard Lianne²,Kaufman Joel D.²,Szpiro Adam A.²

Affiliation:

1. Winona State University; USA

2. University of Washington; Seattle USA

Funder

‘Intramural research program’

National Institutes of Health

National Institute of Environmental Health Sciences

U.S. Environmental Protection Agency

University of Washington

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/rssc.12144/fullpdf

Reference36 articles.

1. A national prediction model for PM2.5 component exposures and measurement error-corrected health effect inference;Bergen;Environ. Hlth Perspect.,2013

2. Mitigating the impact of measurement error when using penalized regression to model exposure in two-stage air pollution epidemiology studies;Bergen;Environ. Ecol. Statist.,2015

3. Estimating the health effects of exposure to multi-pollutant mixture;Billionnet;Ann. Epidem.,2012

4. An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure;Burnett;Environ. Hlth Perspect.,2014

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