Dynamic generalized linear models with application to environmental epidemiology
Dynamic generalized linear models with application to environmental epidemiology
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DOI：
10.1111/1467-9876.00280
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发表时间：
2002-01-01
影响因子：
1.6
通讯作者：
Gaetan, C
中科院分区：
文献类型：
--
作者：
Chiogna, M;Gaetan, C
我们建议使用动态广义线性模型模拟短期污染物暴露对健康的影响。计数数据的时间序列由具有由潜在马尔可夫过程驱动的均值的泊松分布建模;估计由扩展卡尔曼滤波器和平滑器执行。这种建模策略使我们能够考虑协变量可能的过度分散和随时间变化的影响。这些想法通过重新分析亚拉巴马州伯明翰市每日非意外死亡与空气污染之间关系的数据得到了说明。
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.