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Mortality and Air Pollution in Philadelphia: A Dynamic Generalized Linear Modelling Approach

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Abstract

In this paper, we tackle the study of the relationship between daily non accidental deaths and air pollution in the city of Philadelphia in the years 1974 -1988. For modelling the data, we propose to make use of dynamic generalized linear models. These models allow to deal with the serial dependence and time-varying effects of the covariates. Inference is performed by using extended Kaiman filter and smoother.

Keywords

  • Dewpoint Temperature
  • Serial Dependence
  • Time Series Error
  • Standard Generalize Linear Model
  • Order Random Walk

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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© 2004 Springer-Verlag Berlin Heidelberg

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Chiogna, M., Gaetan, C. (2004). Mortality and Air Pollution in Philadelphia: A Dynamic Generalized Linear Modelling Approach. In: Bock, HH., Chiodi, M., Mineo, A. (eds) Advances in Multivariate Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17111-6_19

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  • DOI: https://doi.org/10.1007/978-3-642-17111-6_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20889-1

  • Online ISBN: 978-3-642-17111-6

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