论文标题
PM $ _ {2.5} $浓度通过时间不断发展的协方差模型的建模和预测时空动力学
Modeling and Predicting Spatio-temporal Dynamics of PM$_{2.5}$ Concentrations Through Time-evolving Covariance Models
论文作者
论文摘要
精细的颗粒物（PM $ _ {2.5} $）由于其不良健康影响而成为全世界的一个非常关注的问题。 PM $ _ {2.5} $浓度通常表现出复杂的时空变化。由于季节性，均值和时空依赖性随时间而演变，这使得PM $ _ {2.5} $挑战的统计分析。在地统计学中，高斯过程是表征和预测这种时空动力学的强大工具，为此，时空协方差函数的规范是关键。尽管现存的文献为灵活的固定时空协方差模型提供了广泛的选择，但暂时发展的时空依赖性仅受到了很少的关注。为此，我们提出了一个随时间变化的时空协方差模型，用于描述PM $ _ {2.5} $浓度中的时间不变时空依赖性。为了进行估计，我们开发了一个基于复合的可能性程序来处理大型时空数据集。所提出的模型显示，通过模拟研究，根据预测的模拟研究表现了传统上使用的模型。我们应用模型来分析美国俄勒冈州的PM $ _ {2.5} $数据。在其中，我们表明空间尺度和平滑度表现出周期性。提出的模型还显示出比该数据集上传统使用的模型有益的预测。
Fine particulate matter (PM$_{2.5}$) has become a great concern worldwide due to its adverse health effects. PM$_{2.5}$ concentrations typically exhibit complex spatio-temporal variations. Both the mean and the spatio-temporal dependence evolve with time due to seasonality, which makes the statistical analysis of PM$_{2.5}$ challenging. In geostatistics, Gaussian process is a powerful tool for characterizing and predicting such spatio-temporal dynamics, for which the specification of a spatio-temporal covariance function is the key. While the extant literature offers a wide range of choices for flexible stationary spatio-temporal covariance models, the temporally evolving spatio-temporal dependence has received scant attention only. To this end, we propose a time-varying spatio-temporal covariance model for describing the time-evolving spatio-temporal dependence in PM$_{2.5}$ concentrations. For estimation, we develop a composite likelihood-based procedure to handle large spatio-temporal datasets.The proposed model is shown to outperform traditionally used models through simulation studies in terms of predictions. We apply our model to analyze the PM$_{2.5}$ data in the state of Oregon, US. Therein, we show that the spatial scale and smoothness exhibit periodicity. The proposed model is also shown to be beneficial over traditionally used models on this dataset for predictions.
