• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    Statistically and Computationally Efficient Estimating Equations for Large Spatial Datasets

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    2014.SS.JCGS.pdf
    Size:
    356.7Kb
    Format:
    PDF
    Description:
    Accepted Manuscript
    Download
    Thumbnail
    Name:
    ucgs_a_975230_sm8511.zip
    Size:
    10.53Kb
    Format:
    Unknown
    Description:
    Supplemental files
    Download
    Type
    Article
    Authors
    Sun, Ying cc
    Stein, Michael L.
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Environmental Statistics Group
    Statistics Program
    Date
    2016-03-09
    Online Publication Date
    2016-03-09
    Print Publication Date
    2016-01-02
    Permanent link to this record
    http://hdl.handle.net/10754/552300
    
    Metadata
    Show full item record
    Abstract
    For Gaussian process models, likelihood based methods are often difficult to use with large irregularly spaced spatial datasets, because exact calculations of the likelihood for n observations require O(n3) operations and O(n2) memory. Various approximation methods have been developed to address the computational difficulties. In this paper, we propose new unbiased estimating equations based on score equation approximations that are both computationally and statistically efficient. We replace the inverse covariance matrix that appears in the score equations by a sparse matrix to approximate the quadratic forms, then set the resulting quadratic forms equal to their expected values to obtain unbiased estimating equations. The sparse matrix is constructed by a sparse inverse Cholesky approach to approximate the inverse covariance matrix. The statistical efficiency of the resulting unbiased estimating equations are evaluated both in theory and by numerical studies. Our methods are applied to nearly 90,000 satellite-based measurements of water vapor levels over a region in the Southeast Pacific Ocean.
    Citation
    Statistically and Computationally Efficient Estimating Equations for Large Spatial Datasets 2014:00 Journal of Computational and Graphical Statistics
    Publisher
    Informa UK Limited
    Journal
    Journal of Computational and Graphical Statistics
    DOI
    10.1080/10618600.2014.975230
    Additional Links
    http://www.tandfonline.com/doi/abs/10.1080/10618600.2014.975230
    ae974a485f413a2113503eed53cd6c53
    10.1080/10618600.2014.975230
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.