Mitigating the impact of measurement error when using penalized regression to model exposure in two-stage air pollution epidemiology studies-Reference-Cited by-同舟云学术

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Mitigating the impact of measurement error when using penalized regression to model exposure in two-stage air pollution epidemiology studies

Published:2015-02-14 Issue:3 Volume:22 Page:601-631
ISSN:1352-8505
Container-title:Environmental and Ecological Statistics
language:en
Short-container-title:Environ Ecol Stat

Author:

Bergen Silas,Szpiro Adam A.

Publisher

Springer Science and Business Media LLC

Subject

Statistics, Probability and Uncertainty,General Environmental Science,Statistics and Probability

Link

http://link.springer.com/content/pdf/10.1007/s10651-015-0314-y.pdf

Reference30 articles.

1. Abdi H (2010) Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdiscip Rev Comput Stat 2(1):97–106

2. Bergen S, Sheppard L, Sampson P, Kim S, Richards M, Vedal S, Kaufman J, Szpiro A (2013) A national prediction model for $$\text{ PM }_{2.5}$$ PM 2.5 component exposures and measurement error-corrected health effect inference. Environ Health Perspect 121(9):1017–1025

3. Carroll R (2006) Measurement error in nonlinear models: a modern perspective. CRC Press, Boca Radon

4. Cefalu M, Dominici F (2014) Does exposure prediction bias health-effect estimation? The relationship between confounding adjustment and exposure prediction. Epidemiology 25(4):583–590

5. Chan S, Van Hee V, Bergen S, Szpiro A, Oron A, DeRoo L, London S, Marshall J, Kaufman J, Sandler D (accepted) Long term air pollution exposure and blood pressure in the Sister Study. Environ Health Perspect

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4. In Pursuit of Evidence in Air Pollution Epidemiology: The Role of Causally Driven Data Science;Epidemiology;2020-01

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