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Synthetically lagged models

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  • Burr, Wesley S.
  • Shin, Hwashin H.
  • Takahara, Glen

Abstract

Epidemiologic studies often report health risk due to short-term air pollution exposure through examination of lagged association, expressing manifestation of population health effects over time. We develop a new approach to the problem of lagged manifestation of air pollution exposure: synthetically lagged models. This approach also generalizes to other lagged time series regression models.

Suggested Citation

  • Burr, Wesley S. & Shin, Hwashin H. & Takahara, Glen, 2019. "Synthetically lagged models," Statistics & Probability Letters, Elsevier, vol. 144(C), pages 37-43.
    • Handle: RePEc:eee:stapro:v:144:y:2019:i:c:p:37-43
    DOI: 10.1016/j.spl.2018.07.008
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    References listed on IDEAS

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    1. Francesca Dominici & Jonathan M. Samet & Scott L. Zeger, 2000. "Combining evidence on air pollution and daily mortality from the 20 largest US cities: a hierarchical modelling strategy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 263-302.
    2. L. J. Welty & R. D. Peng & S. L. Zeger & F. Dominici, 2009. "Bayesian Distributed Lag Models: Estimating Effects of Particulate Matter Air Pollution on Daily Mortality," Biometrics, The International Biometric Society, vol. 65(1), pages 282-291, March.
    3. Wesley S. Burr & Glen Takahara & Hwashin H. Shin, 2015. "Bias correction in estimation of public health risk attributable to short‐term air pollution exposure," Environmetrics, John Wiley & Sons, Ltd., vol. 26(4), pages 298-311, June.
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