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A model-based approach for imputing censored data in source apportionment studies.
Environmental and Ecological Statistics ( IF 3.8 ) Pub Date : 2015-06-04 , DOI: 10.1007/s10651-015-0319-6
Jenna R Krall 1 , Charles H Simpson 2 , Roger D Peng 3
Affiliation  

Sources of particulate matter (PM) air pollution are generally inferred from PM chemical constituent concentrations using source apportionment models. Concentrations of PM constituents are often censored below minimum detection limits (MDL) and most source apportionment models cannot handle these censored data. Frequently, censored data are first substituted by a constant proportion of the MDL or are removed to create a truncated dataset before sources are estimated. When estimating the complete data distribution, these commonly applied methods to adjust censored data perform poorly compared with model-based imputation methods. Model-based imputation has not been used in source apportionment and may lead to better source estimation. However if the censored chemical constituents are not important for estimating sources, censoring adjustment methods may have little impact on source estimation. We focus on two source apportionment models applied in the literature and provide a comprehensive assessment of how censoring adjustment methods, including model-based imputation, impact source estimation. A review of censoring adjustment methods critically informs how censored data should be handled in these source apportionment models. In a simulation study, we demonstrated that model-based multiple imputation frequently leads to better source estimation compared with commonly used censoring adjustment methods. We estimated sources of PM in New York City and found estimated source distributions differed by censoring adjustment method. In this study, we provide guidance for adjusting censored PM constituent data in common source apportionment models, which is necessary for estimation of PM sources and their subsequent health effects.

中文翻译:

一种基于模型的方法,用于在源分配研究中估算检查数据。

通常使用污染源分配模型从PM化学成分浓度推断出颗粒物(PM)空气污染源。通常将PM成分的浓度检查在最低检出限(MDL)以下,并且大多数源分配模型无法处理这些检查数据。通常,在估计源之前,先用恒定比例的MDL替换受检查的数据,或者将其删除以创建截断的数据集。估计完整的数据分布时,这些用于调整检查数据的常用方法与基于模型的插补方法相比效果较差。基于模型的归因尚未在源分配中使用,并且可能导致更好的源估计。但是,如果被检查的化学成分对于估算来源不重要,审查调整方法可能对源估计几乎没有影响。我们集中于文献中应用的两个源分配模型,并提供对如何审查调整方法的全面评估,包括基于模型的估算,影响源估计。对审查调整方法的审查可以从关键角度告知在这些源分配模型中应如何处理审查数据。在模拟研究中,我们证明了与常用的检查调整方法相比,基于模型的多重插补经常会导致更好的源估计。我们估算了纽约市的PM来源,并发现根据检查调整方法估算的来源分布有所不同。在这项研究中,我们提供了在常见来源分配模型中调整被检查的PM构成数据的指导,
更新日期:2015-06-04
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