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Frequency domain log-linear models, air pollution and mortality.

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Frequency domain log-linear models, air pollution and mortality. / Kelsall, J. E.; Samet, J. M.; Zeger, S. L.
In: Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 48, No. 3, 1999, p. 331-344.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Kelsall, JE, Samet, JM & Zeger, SL 1999, 'Frequency domain log-linear models, air pollution and mortality.', Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 48, no. 3, pp. 331-344. https://doi.org/10.1111/1467-9876.00156

APA

Kelsall, J. E., Samet, J. M., & Zeger, S. L. (1999). Frequency domain log-linear models, air pollution and mortality. Journal of the Royal Statistical Society: Series C (Applied Statistics), 48(3), 331-344. https://doi.org/10.1111/1467-9876.00156

Vancouver

Kelsall JE, Samet JM, Zeger SL. Frequency domain log-linear models, air pollution and mortality. Journal of the Royal Statistical Society: Series C (Applied Statistics). 1999;48(3):331-344. doi: 10.1111/1467-9876.00156

Author

Kelsall, J. E. ; Samet, J. M. ; Zeger, S. L. / Frequency domain log-linear models, air pollution and mortality. In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 1999 ; Vol. 48, No. 3. pp. 331-344.

Bibtex

@article{6d31ea73e2344b6faedcb3d2168f858a,
title = "Frequency domain log-linear models, air pollution and mortality.",
abstract = "Motivated by a study of the association between counts of daily mortality and air pollution, we present a frequency domain estimation approach for log-linear models that accounts for both overdispersion and autocorrelation. The methods also allow for the discounting or downweighting of information at particular frequencies at which, for example, confounding variables are likely to have greatest influence. This allows flexible sensitivity analyses to be carried out to assess the possible effect of confounders on the estimated effect. We apply the methods to estimate the association between counts of mortality and the concentration of airborne particles in Philadelphia, USA, for the years 1974–1988. We obtain an estimated effect of particulate air pollution on mortality that is significantly greater than zero but less than that obtained by a standard log-linear analysis.",
keywords = "Air pollution • Autocorrelation • Frequency domain • Overdispersion • Poisson regression",
author = "Kelsall, {J. E.} and Samet, {J. M.} and Zeger, {S. L.}",
year = "1999",
doi = "10.1111/1467-9876.00156",
language = "English",
volume = "48",
pages = "331--344",
journal = "Journal of the Royal Statistical Society: Series C (Applied Statistics)",
issn = "0035-9254",
publisher = "Wiley-Blackwell",
number = "3",

}

RIS

TY - JOUR

T1 - Frequency domain log-linear models, air pollution and mortality.

AU - Kelsall, J. E.

AU - Samet, J. M.

AU - Zeger, S. L.

PY - 1999

Y1 - 1999

N2 - Motivated by a study of the association between counts of daily mortality and air pollution, we present a frequency domain estimation approach for log-linear models that accounts for both overdispersion and autocorrelation. The methods also allow for the discounting or downweighting of information at particular frequencies at which, for example, confounding variables are likely to have greatest influence. This allows flexible sensitivity analyses to be carried out to assess the possible effect of confounders on the estimated effect. We apply the methods to estimate the association between counts of mortality and the concentration of airborne particles in Philadelphia, USA, for the years 1974–1988. We obtain an estimated effect of particulate air pollution on mortality that is significantly greater than zero but less than that obtained by a standard log-linear analysis.

AB - Motivated by a study of the association between counts of daily mortality and air pollution, we present a frequency domain estimation approach for log-linear models that accounts for both overdispersion and autocorrelation. The methods also allow for the discounting or downweighting of information at particular frequencies at which, for example, confounding variables are likely to have greatest influence. This allows flexible sensitivity analyses to be carried out to assess the possible effect of confounders on the estimated effect. We apply the methods to estimate the association between counts of mortality and the concentration of airborne particles in Philadelphia, USA, for the years 1974–1988. We obtain an estimated effect of particulate air pollution on mortality that is significantly greater than zero but less than that obtained by a standard log-linear analysis.

KW - Air pollution • Autocorrelation • Frequency domain • Overdispersion • Poisson regression

U2 - 10.1111/1467-9876.00156

DO - 10.1111/1467-9876.00156

M3 - Journal article

VL - 48

SP - 331

EP - 344

JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)

JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)

SN - 0035-9254

IS - 3

ER -