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    Efficient Estimation of Non-stationary Spatial Covariance Functions with Application to High-resolution Climate Model Emulation

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    Type
    Article
    Authors
    Li, Yuxiao
    Sun, Ying cc
    KAUST Department
    Statistics Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2018-10-08
    Online Publication Date
    2018-10-08
    Print Publication Date
    2019
    Embargo End Date
    2019-10-08
    Permanent link to this record
    http://hdl.handle.net/10754/656333
    
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    Abstract
    Spatial processes exhibit nonstationarity in many climate and environmental applications. Convolution-based approaches are often used to construct nonstationary covariance functions in Gaussian processes. Although convolution-based models are flexible, their computation is extremely expensive when the data set is large. Most existing methods rely on fitting an anisotropic, but stationary model locally, and then reconstructing the spatially varying parameters. In this study, we propose a new estimation procedure to approximate a class of nonstationary Matérn covariance functions by local-polynomial fitting the covariance parameters. The proposed method allows for efficient estimation of a richer class of nonstationary covariance functions, with the local stationary model as a special case. We also develop an approach for a fast high-resolution simulation with nonstationary features on a small scale and apply it to precipitation data in climate model outputs.
    Citation
    Li, Y., & Sun, Y. (2019). Efficient Estimation of Non-stationary Spatial Covariance Functions with Application to High-resolution Climate Model Emulation. Statistica Sinica. doi:10.5705/ss.202017.0536
    Sponsors
    This research was supported by funding from King Abdullah University of Science and Technology (KAUST). We would like to thank the editor, associate editor, and two anonymous reviewers for their valuable comments.
    Publisher
    Institute of Statistical Science
    Journal
    Statistica Sinica
    DOI
    10.5705/ss.202017.0536
    Additional Links
    http://www3.stat.sinica.edu.tw/statistica/J29N3/J29N36/J29N36.html
    ae974a485f413a2113503eed53cd6c53
    10.5705/ss.202017.0536
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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