论文标题
EPA颗粒物数据 - 使用本地控制策略进行分析
EPA Particulate Matter Data -- Analyses using Local Control Strategy
论文作者
论文摘要
当所使用的方法既无参数又无监督时，用于分析大量横截面观察数据的统计学习方法最有效。我们说明了在2016年美国环境流行病学数据上使用NU学习方法的使用。我们鼓励其他研究人员下载这些数据，应用他们希望的任何方法，并为开发基于二次有机气溶胶的潜在影响（主要是生物源或人为起源的挥发性有机化合物）的潜在影响的广泛``共识观点''的发展。我们在这里的分析重点介绍了一个问题：````有相对较高的空气生物颗粒物质的地区是否也有望具有相对较高的循环和/或呼吸道死亡？'''''
Statistical Learning methodology for analysis of large collections of cross-sectional observational data can be most effective when the approach used is both Nonparametric and Unsupervised. We illustrate use of our NU Learning approach on 2016 US environmental epidemiology data that we have made freely available. We encourage other researchers to download these data, apply whatever methodology they wish, and contribute to development of a broad-based ``consensus view'' of potential effects of Secondary Organic Aerosols (volatile organic compounds of predominantly biogenic or anthropogenic origin) within PM2.5 particulate matter on circulatory and/or respiratory mortality. Our analyses here focus on the question: ``Are regions with relatively high air-borne biogenic particulate matter also expected to have relatively high circulatory and/or respiratory mortality?''
