Modeling Temporally Evolving and Spatially Globally Dependent Data
Modeling Temporally Evolving and Spatially Globally Dependent Data
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DOI：
10.1111/insr.12266
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发表时间：
2018-08-01
影响因子：
2
通讯作者：
Furrer, Reinhard
中科院分区：
文献类型：
--
作者：
Porcu, Emilio;Alegria, Alfredo;Furrer, Reinhard
在过去几十年中，从遥感网络到气候模型集合，具有全球性和随时间变化的数据集的可用性空前增加。本文概述了时空过程的统计建模技术，其中空间是代表我们星球的球体。特别是，我们之间的区分（a）二阶为基础的方法和（B）的实际方法来建模时间演变的全球过程。前一种方法是基于一类时空协方差函数的规范，空间是二维球。后者是基于显式描述的时空过程的动态，也就是说，通过指定其演变作为其过去的历史与空间相关的噪声的函数。我们主要关注方法（a），文献很少。我们提供了新的模型的时空协方差函数的随机场上定义的球交叉时间。还讨论了实用方法（B），特别强调直接在球体上建立模型，而不将球坐标投影到平面上。我们提出了一个案例研究，重点分析了2015年赤道亚洲野火的空气污染，这一事件被列为今年最严重的环境灾难。本文最后列出了该领域的主要理论和应用研究问题，我们希望统计界在未来十年内参与其中。
The last decades have seen an unprecedented increase in the availability of data sets that are inherently global and temporally evolving, from remotely sensed networks to climate model ensembles. This paper provides an overview of statistical modeling techniques for space-time processes, where space is the sphere representing our planet. In particular, we make a distintion between (a) second order-based approaches and (b) practical approaches to modeling temporally evolving global processes. The former approaches are based on the specification of a class of space-time covariance functions, with space being the two-dimensional sphere. The latter are based on explicit description of the dynamics of the space-time process, that is, by specifying its evolution as a function of its past history with added spatially dependent noise. We focus primarily on approach (a), for which the literature has been sparse. We provide new models of space-time covariance functions for random fields defined on spheres cross time. Practical approaches (b) are also discussed, with special emphasis on models built directly on the sphere, without projecting spherical coordinates onto the plane. We present a case study focused on the analysis of air pollution from the 2015 wildfires in Equatorial Asia, an event that was classified as the year's worst environmental disaster. The paper finishes with a list of the main theoretical and applied research problems in the area, where we expect the statistical community to engage over the next decade.