Ludwig Guilherme, Chu Tingjin, Zhu Jun, Wang Haonan, Koehler Kirsten
Ann Appl Stat. 2017 Mar;11(1):139-160. doi: 10.1214/16-AOAS995. Epub 2017 Apr 8.
Rapid technological advances have drastically improved the data collection capacity in occupational exposure assessment. However, advanced statistical methods for analyzing such data and drawing proper inference remain limited. The objectives of this paper are (1) to provide new spatio-temporal methodology that combines data from both roving and static sensors for data processing and hazard mapping across space and over time in an indoor environment, and (2) to compare the new method with the current industry practice, demonstrating the distinct advantages of the new method and the impact on occupational hazard assessment and future policy making in environmental health as well as occupational health. A novel spatio-temporal model with a continuous index in both space and time is proposed, and a profile likelihood-based model fitting procedure is developed that allows fusion of the two types of data. To account for potential differences between the static and roving sensors, we extend the model to have nonhomogenous measurement error variances. Our methodology is applied to a case study conducted in an engine test facility, and dynamic hazard maps are drawn to show features in the data that would have been missed by existing approaches, but are captured by the new method.
快速的技术进步极大地提高了职业暴露评估中的数据收集能力。然而，用于分析此类数据并得出恰当推断的先进统计方法仍然有限。本文的目标是：（1）提供一种新的时空方法，该方法结合来自移动传感器和静态传感器的数据，用于室内环境中跨空间和时间的数据处理及危害映射；（2）将新方法与当前行业实践进行比较，展示新方法的显著优势以及对职业危害评估和未来环境健康与职业健康政策制定的影响。提出了一种在空间和时间上都具有连续索引的新型时空模型，并开发了一种基于轮廓似然的模型拟合程序，该程序允许融合这两种类型的数据。为了考虑静态传感器和移动传感器之间的潜在差异，我们扩展模型以具有非齐次测量误差方差。我们的方法应用于在发动机测试设施中进行的一个案例研究，并绘制了动态危害图，以展示现有方法会遗漏但新方法能捕捉到的数据特征。
