A Bayesian model averaging approach for estimating the relative risk of mortality associated with heat waves in 105 U.S. cities.
A Bayesian model averaging approach for estimating the relative risk of mortality associated with heat waves in 105 U.S. cities.
复制标题
贝叶斯模型平均方法是估计105个城市中与热浪相关的死亡率相对的相对风险。
DOI：
10.1111/j.1541-0420.2011.01583.x
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发表时间：
2011-12
期刊：
影响因子：
1.9
通讯作者：
Peng RD
中科院分区：
文献类型：
--
作者：
Bobb JF;Dominici F;Peng RD
估计热浪对人类健康构成的风险是评估气候变化未来影响的关键部分。在本文中，我们提出了一类灵活的时间序列模型来估计与热浪相关的死亡的相对风险，并进行了贝叶斯模型平均(BMA)来解释潜在模型的多样性。将这些方法应用于美国105个城市1987-2005年的数据，我们确定了那些在热浪中死亡风险增加的后验概率很高的城市，检查了各城市死亡风险后验分布的异质性，评估了结果对先验分布选择的敏感性，并将我们的BMA结果与模型选择方法进行了比较。我们的结果表明，没有一个单一的模型可以最好地预测大多数城市的风险，而且对于一些城市来说，热浪风险估计对模型的选择很敏感。虽然模型平均导致后验分布的方差增加，但我们发现，与通过模型选择获得的模型的统计推断相比，热浪死亡风险的后验均值对于解释大类模型的不确定性是稳健的。
Estimating the risks heat waves pose to human health is a critical part of assessing the future impact of climate change. In this paper we propose a flexible class of time series models to estimate the relative risk of mortality associated with heat waves and conduct Bayesian model averaging (BMA) to account for the multiplicity of potential models. Applying these methods to data from 105 U.S. cities for the period 1987–2005, we identify those cities having a high posterior probability of increased mortality risk during heat waves, examine the heterogeneity of the posterior distributions of mortality risk across cities, assess sensitivity of the results to the selection of prior distributions, and compare our BMA results to a model selection approach. Our results show that no single model best predicts risk across the majority of cities, and that for some cities heat wave risk estimation is sensitive to model choice. While model averaging leads to posterior distributions with increased variance as compared to statistical inference conditional on a model obtained through model selection, we find that the posterior mean of heat wave mortality risk is robust to accounting for model uncertainty over a broad class of models.
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