论文标题
在网络交叉时间上的不可分割的时空固定协方差功能
Nonseparable Space-Time Stationary Covariance Functions on Networks cross Time
论文作者
论文摘要
数据科学的出现为高数据复杂性提供了越来越多的挑战。本文解决了时空数据的挑战，其中空间域不是平面表面，球体或线性网络，而是广义网络（称为具有欧几里得边缘的图）。此外，在不同的时间瞬间反复测量数据。我们提供新的类别的不可分割的时空固定协方差函数，其中{\ em Space}可以是广义网络，欧几里得树或线性网络，并且时间可以是线性或圆形（季节性）。由于施工原则是技术性的，因此我们专注于指导读者构建统计解释的例子的插图。一项仿真研究表明，与拼配的模型相比，我们可以恢复正确的模型。此外，我们的模拟研究表明，我们有效地恢复了仿真参数。在我们的数据分析中，我们考虑了一个流量事故数据集，该数据集显示了基于协方差规范和基于网络的指标的改进模型性能。
The advent of data science has provided an increasing number of challenges with high data complexity. This paper addresses the challenge of space-time data where the spatial domain is not a planar surface, a sphere, or a linear network, but a generalized network (termed a graph with Euclidean edges). Additionally, data are repeatedly measured over different temporal instants. We provide new classes of nonseparable space-time stationary covariance functions where {\em space} can be a generalized network, a Euclidean tree, or a linear network, and where time can be linear or circular (seasonal). Because the construction principles are technical, we focus on illustrations that guide the reader through the construction of statistically interpretable examples. A simulation study demonstrates that we can recover the correct model when compared to misspecified models. In addition, our simulation studies show that we effectively recover simulation parameters. In our data analysis, we consider a traffic accident dataset that shows improved model performance based on covariance specifications and network-based metrics.
