Space-Time Covariance Structures and Models

被引:29
作者
Chen, Wanfang [1 ]
Genton, Marc G. [1 ]
Sun, Ying [1 ]
机构
[1] King Abdullah Univ Sci & Technol, Stat Program, Thuwal 239556900, Saudi Arabia
来源
ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 8, 2021 | 2021年 / 8卷
关键词
asymmetry; full symmetry; kriging; positive definiteness; random field; separability; stationarity; LIKELIHOOD RATIO TEST; RANDOM-FIELDS; SPATIOTEMPORAL COVARIANCE; NONSTATIONARY; SEPARABILITY; STATIONARY; SYMMETRY; TESTS; NONSEPARABILITY; GEOSTATISTICS;
D O I
10.1146/annurev-statistics-042720-115603
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
In recent years, interest has grown in modeling spatio-temporal data generated from monitoring networks, satellite imaging, and climate models. Under Gaussianity, the covariance function is core to spatio-temporal modeling, inference, and prediction. In this article, we review the various space-time covariance structures in which simplified assumptions, such as separability and full symmetry, are made to facilitate computation, and associated tests intended to validate these structures. We also review recent developments on constructing space-time covariance models, which can be separable or nonseparable, fully symmetric or asymmetric, stationary or nonstationary, univariate or multivariate, and in Euclidean spaces or on the sphere. We visualize some of the structures and models with visuanimations. Finally, we discuss inference for fitting space-time covariance models and describe a case study based on a new wind-speed data set.
引用
收藏
页码:191 / 215
页数:25
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