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Ensemble post-processing is a promising method to obtain flexible distributed lag models

Journal Contribution - Journal Article

Subtitle:A simulation study of time series of air pollution and daily mortality
Distributed lag models (DLM) are regression models that include multiple lagged exposure variables as covariates. They are frequently used to model the relationship between daily mortality and short term air pollution exposures. Specifying a maximum lag number is but one of the difficulties in using a DLM for environmental epidemiology. We propose an easily extendible ensemble post-processing approach. The resultant estimates are both more parsimonious, approaching zero with increasing lag, and more efficient. The benefits are shown to be robust under various simulation scenario's and illustrated with data from the National Morbidity, Mortality and Air Pollution Study.
Journal: Air Quality, Atmosphere & Health
ISSN: 1873-9318
Issue: 7
Volume: 9
Pages: 835-846
Publication year:2016
CSS-citation score:1
Accessibility:Closed