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    Collective spectral density estimation and clustering for spatially-correlated data

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    Collective_1-s2.0-S2211675320300452-main.pdf
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    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Chen, Tianbo cc
    Sun, Ying cc
    Maadooliat, Mehdi cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Environmental Statistics Group
    Statistics Program
    Date
    2020-05-16
    Online Publication Date
    2020-05-16
    Print Publication Date
    2020-08
    Embargo End Date
    2022-05-16
    Submitted Date
    2019-06-11
    Permanent link to this record
    http://hdl.handle.net/10754/662866
    
    Metadata
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    Abstract
    In this paper, we develop a method for estimating and clustering two-dimensional spectral density functions (2D-SDFs) for spatial data from multiple subregions. We use a common set of adaptive basis functions to explain the similarities among the 2D-SDFs in a low-dimensional space and estimate the basis coefficients by maximizing the Whittle likelihood with two penalties. We apply these penalties to impose the smoothness of the estimated 2D-SDFs and the spatial dependence of the spatially-correlated subregions. The proposed technique provides a score matrix, that is comprised of the estimated coefficients associated with the common set of basis functions representing the 2D-SDFs. Instead of clustering the estimated SDFs directly, we propose to employ the score matrix for clustering purposes, taking advantage of its low-dimensional property. In a simulation study, we demonstrate that our proposed method outperforms other competing estimation procedures used for clustering. Finally, to validate the described clustering method, we apply the procedure to soil moisture data from the Mississippi basin to produce homogeneous spatial clusters. We produce animations to dynamically show the estimation procedure, including the estimated 2D-SDFs and the score matrix, which provide an intuitive illustration of the proposed method.
    Citation
    Chen, T., Sun, Y., & Maadooliat, M. (2020). Collective spectral density estimation and clustering for spatially-correlated data. Spatial Statistics, 100451. doi:10.1016/j.spasta.2020.100451
    Sponsors
    The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) to Ying Sun and Tianbo Chen. We would also like to thank the editor, and two referees for their constructive and thoughtful comments which helped us tremendously in improving the manuscript.
    Publisher
    Elsevier BV
    Journal
    Spatial Statistics
    DOI
    10.1016/j.spasta.2020.100451
    arXiv
    2007.14085
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S2211675320300452
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.spasta.2020.100451
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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