School of Finance and Applied Statistics, College of Business and Economics, Australian National University, Australian Capital Territory, Australia.
Environ Health Perspect. 2010 Jan;118(1):131-6. doi: 10.1289/ehp.0901007.
Concerns have been raised about findings of associations between particulate matter (PM) air pollution and mortality that have been based on a single "best" model arising from a model selection procedure, because such a strategy may ignore model uncertainty inherently involved in searching through a set of candidate models to find the best model. Model averaging has been proposed as a method of allowing for model uncertainty in this context.
To propose an extension (double BOOT) to a previously described bootstrap model-averaging procedure (BOOT) for use in time series studies of the association between PM and mortality. We compared double BOOT and BOOT with Bayesian model averaging (BMA) and a standard method of model selection [standard Akaike's information criterion (AIC)].
Actual time series data from the United States are used to conduct a simulation study to compare and contrast the performance of double BOOT, BOOT, BMA, and standard AIC.
Double BOOT produced estimates of the effect of PM on mortality that have had smaller root mean squared error than did those produced by BOOT, BMA, and standard AIC. This performance boost resulted from estimates produced by double BOOT having smaller variance than those produced by BOOT and BMA.
Double BOOT is a viable alternative to BOOT and BMA for producing estimates of the mortality effect of PM.
人们对基于模型选择过程中单一“最佳”模型得出的颗粒物（PM）空气污染与死亡率之间存在关联的发现表示担忧，因为这种策略可能忽略了在一组候选模型中寻找最佳模型时固有的模型不确定性。模型平均已被提议作为在这种情况下允许模型不确定性的一种方法。
针对 PM 与死亡率之间关联的时间序列研究，提出先前描述的自举模型平均过程（BOOT）的扩展（双 BOOT）。我们比较了双 BOOT、BOOT、贝叶斯模型平均（BMA）和标准模型选择方法（标准赤池信息量准则（AIC））。
使用来自美国的实际时间序列数据进行模拟研究，以比较和对比双 BOOT、BOOT、BMA 和标准 AIC 的性能。
双 BOOT 产生的 PM 对死亡率影响的估计值，其均方根误差比 BOOT、BMA 和标准 AIC 产生的估计值小。这种性能提升源于双 BOOT 产生的估计值比 BOOT 和 BMA 产生的估计值具有更小的方差。
双 BOOT 是 BOOT 和 BMA 的一种可行替代方法，可用于生成 PM 对死亡率影响的估计值。
