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Bootstrap model averaging in time series studies of particulate matter air pollution and mortality

Journal of Exposure Science & Environmental Epidemiology volume 16, pages 242–250 (2006)Cite this article

Abstract

The consensus from time series studies that have investigated the mortality effects of particulate matter air pollution (PM) is that increases in PM are associated with increases in daily mortality. However, recently concerns have been raised that the observed positive association between PM and mortality may be an artefact of model selection due to multiple hypothesis testing. This problem arises when a number of models are investigated, but only the “best” model is reported and all subsequent inference is based on this model, ignoring the model selection process. In this paper, we introduce the use of the bootstrap as a means of addressing the problems of model selection in PM mortality time series studies. Using the bootstrap to perform inference about the effect of PM on mortality is a process based on a set of models rather than on a single model. It is shown that using the bootstrap to overcome the problems of model selection is competitive with the existing methodology of Bayesian model averaging.

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Authors and Affiliations

  1. School of Finance and Applied Statistics, Faculty of Economics and Commerce, Australian National University, Canberra, Australia

    Michael A Martin & Steven Roberts

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  1. Michael A Martin
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  2. Steven Roberts
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Correspondence to Steven Roberts.

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Martin, M., Roberts, S. Bootstrap model averaging in time series studies of particulate matter air pollution and mortality. J Expo Sci Environ Epidemiol 16, 242–250 (2006). https://doi.org/10.1038/sj.jea.7500454

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