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Dynamic generalized linear models with application to environmental epidemiology
Author(s) -
Chiogna and Carlo Gaetan Monica,
Gaetan Carlo
Publication year - 2002
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/1467-9876.00280
Subject(s) - overdispersion , kalman filter , generalized linear model , covariate , econometrics , poisson distribution , count data , poisson regression , statistics , quasi likelihood , computer science , mathematics , environmental health , medicine , population
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.