Dynamic generalized linear models with application to environmental epidemiology

Monica Chiogna and Carlo Gaetan and Carlo Gaetan

Journal of the Royal Statistical Society Series C, 2002, vol. 51, issue 4, 453-468

Abstract: Summary. We propose modelling short‐term pollutant exposure effects on health by using dynamic generalized linear models. The time series of count data are modelled by a Poisson distribution having mean driven by a latent Markov process; estimation is performed by the extended Kalman filter and smoother. This modelling strategy allows us to take into account possible overdispersion and time‐varying effects of the covariates. These ideas are illustrated by reanalysing data on the relationship between daily non‐accidental deaths and air pollution in the city of Birmingham, Alabama.

Date: 2002
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https://doi.org/10.1111/1467-9876.00280

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