Olives Casey, Sheppard Lianne, Lindström Johan, Sampson Paul D, Kaufman Joel D, Szpiro Adam A
University of Washington.
Lund University.
Ann Appl Stat. 2014 Dec;8(4):2509-2537. doi: 10.1214/14-AOAS786. Epub 2014 Dec 19.
There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a flexible spatio-temporal prediction model that integrates land-use regression with kriging to account for spatial dependence in pollutant concentrations. Temporal variability is captured using temporal trends estimated via modified singular value decomposition and temporally varying spatial residuals. This model utilizes monitoring data from existing regulatory networks and supplementary MESA Air monitoring data to predict concentrations for individual cohort members. In general, spatio-temporal models are limited in their efficacy for large data sets due to computational intractability. We develop reduced-rank versions of the MESA Air spatio-temporal model. To do so, we apply low-rank kriging to account for spatial variation in the mean process and discuss the limitations of this approach. As an alternative, we represent spatial variation using thin plate regression splines. We compare the performance of the outlined models using EPA and MESA Air monitoring data for predicting concentrations of oxides of nitrogen (NO )-a pollutant of primary interest in MESA Air-in the Los Angeles metropolitan area via cross-validated . Our findings suggest that use of reduced-rank models can improve computational efficiency in certain cases. Low-rank kriging and thin plate regression splines were competitive across the formulations considered, although TPRS appeared to be more robust in some settings.
流行病学文献中越来越多的证据表明空气污染与不良健康结果之间存在关联。在环境保护局（EPA）资助的动脉粥样硬化与空气污染多民族研究（MESA Air）中，对个体空气污染暴露的预测依赖于一个灵活的时空预测模型，该模型将土地利用回归与克里金法相结合，以考虑污染物浓度的空间依赖性。使用通过修正奇异值分解估计的时间趋势和随时间变化的空间残差来捕捉时间变异性。该模型利用现有监管网络的监测数据和MESA Air补充监测数据来预测各个队列成员的污染物浓度。一般来说，由于计算上的难处理性，时空模型在处理大数据集时效率有限。我们开发了MESA Air时空模型的降秩版本。为此，我们应用低秩克里金法来考虑均值过程中的空间变化，并讨论这种方法的局限性。作为一种替代方法，我们使用薄板回归样条来表示空间变化。我们通过交叉验证，使用EPA和MESA Air监测数据比较所概述模型在预测洛杉矶大都市区氮氧化物（NO ）浓度方面的性能，氮氧化物是MESA Air中主要关注的污染物。我们的研究结果表明，在某些情况下，使用降秩模型可以提高计算效率。在所考虑的各种公式中，低秩克里金法和薄板回归样条具有竞争力，尽管薄板回归样条在某些情况下似乎更稳健。
