Martin Michael A, Roberts Steven
School of Finance and Applied Statistics, Faculty of Economics and Commerce, Australian National University, Canberra, Australia.
J Expo Sci Environ Epidemiol. 2006 May;16(3):242-50. doi: 10.1038/sj.jea.7500454.
The consensus from time series studies that have investigated the mortality effects of particulate matter air pollution (PM) is that increases in PM are associated with increases in daily mortality. However, recently concerns have been raised that the observed positive association between PM and mortality may be an artefact of model selection due to multiple hypothesis testing. This problem arises when a number of models are investigated, but only the "best" model is reported and all subsequent inference is based on this model, ignoring the model selection process. In this paper, we introduce the use of the bootstrap as a means of addressing the problems of model selection in PM mortality time series studies. Using the bootstrap to perform inference about the effect of PM on mortality is a process based on a set of models rather than on a single model. It is shown that using the bootstrap to overcome the problems of model selection is competitive with the existing methodology of Bayesian model averaging.
对颗粒物空气污染（PM）死亡率影响进行调查的时间序列研究得出的共识是，PM增加与每日死亡率上升相关。然而，最近有人担心，观察到的PM与死亡率之间的正相关可能是由于多重假设检验导致的模型选择假象。当研究多个模型，但只报告“最佳”模型，且所有后续推断都基于该模型，而忽略模型选择过程时，就会出现这个问题。在本文中，我们介绍使用自助法来解决PM死亡率时间序列研究中的模型选择问题。使用自助法对PM对死亡率的影响进行推断是一个基于一组模型而非单个模型的过程。结果表明，使用自助法克服模型选择问题与现有的贝叶斯模型平均方法具有竞争力。
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