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    Space-Time Covariance Structures and Models

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    Name:
    1Template-for-Authors-ChenGenSunRev2-2 (4).pdf
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    471.7Kb
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    PDF
    Description:
    Accepted manuscript
    Embargo End Date:
    2021-10-16
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    Type
    Article
    Authors
    Chen, Wanfang cc
    Genton, Marc G. cc
    Sun, Ying cc
    KAUST Department
    Statistics Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    KAUST Grant Number
    OSR-2018-CRG7-3742.
    Date
    2020-10-16
    Embargo End Date
    2021-10-16
    Submitted Date
    2020-10-16
    Permanent link to this record
    http://hdl.handle.net/10754/667271
    
    Metadata
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    Abstract
    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. Expected final online publication date for the Annual Review of Statistics, Volume 8 is March 8, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
    Citation
    Chen, W., Genton, M. G., & Sun, Y. (2020). Space-Time Covariance Structures and Models. Annual Review of Statistics and Its Application, 8(1). doi:10.1146/annurev-statistics-042720-115603
    Sponsors
    This publication is based on research supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2018-CRG7-3742.
    Publisher
    Annual Reviews
    Journal
    Annual Review of Statistics and Its Application
    DOI
    10.1146/annurev-statistics-042720-115603
    Additional Links
    https://www.annualreviews.org/doi/10.1146/annurev-statistics-042720-115603
    ae974a485f413a2113503eed53cd6c53
    10.1146/annurev-statistics-042720-115603
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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