论文标题
高频监控数据的灵活的非平稳时空建模
Flexible nonstationary spatio-temporal modeling of high-frequency monitoring data
论文作者
论文摘要
许多物理数据集都是通过定期时间间隔进行测量的仪器的集合而生成的。对于此类常规监视数据，我们将半光谱协方差函数的框架扩展到了时空中的非平稳性的情况，并证明了这种方法提供了一种自然而可拖延的方式，可以将复杂的行为纳入协方差模型。此外，我们将此方法与完全时域的计算一起获得了真正的最大似然估计器 - 例如，与使用小型型可能性近似值相反，仍然可以有效地计算出来。我们将此方法应用于非常高频的多普勒激光雷达垂直风速测量值，表明该模型可以表达在大气边界层上方和下方的动力学的极端非平稳性，更重要的是，更重要的是，过程动力学的相互作用。
Many physical datasets are generated by collections of instruments that make measurements at regular time intervals. For such regular monitoring data, we extend the framework of half-spectral covariance functions to the case of nonstationarity in space and time and demonstrate that this method provides a natural and tractable way to incorporate complex behaviors into a covariance model. Further, we use this method with fully time-domain computations to obtain bona fide maximum likelihood estimators---as opposed to using Whittle-type likelihood approximations, for example---that can still be computed efficiently. We apply this method to very high-frequency Doppler LIDAR vertical wind velocity measurements, demonstrating that the model can expressively capture the extreme nonstationarity of dynamics above and below the atmospheric boundary layer and, more importantly, the interaction of the process dynamics across it.
