• Login
    View Item 
    •   Home
    • Office of Sponsored Research (OSR)
    • KAUST Funded Research
    • Publications Acknowledging KAUST Support
    • View Item
    •   Home
    • Office of Sponsored Research (OSR)
    • KAUST Funded Research
    • Publications Acknowledging KAUST Support
    • 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

    Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Article
    Authors
    Zhang, Bohai
    Sang, Huiyan
    Huang, Jianhua Z.
    KAUST Grant Number
    KUS-CI-016-04
    Date
    2014
    Permanent link to this record
    http://hdl.handle.net/10754/598377
    
    Metadata
    Show full item record
    Abstract
    Various continuously-indexed spatio-temporal process models have been constructed to characterize spatio-temporal dependence structures, but the computational complexity for model fitting and predictions grows in a cubic order with the size of dataset and application of such models is not feasible for large datasets. This article extends the full-scale approximation (FSA) approach by Sang and Huang (2012) to the spatio-temporal context to reduce computational complexity. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is proposed to select knots automatically from a discrete set of spatio-temporal points. Our approach is applicable to nonseparable and nonstationary spatio-temporal covariance models. We illustrate the effectiveness of our method through simulation experiments and application to an ozone measurement dataset.
    Citation
    Zhang B, Sang H, Huang JZ (2014) Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets. STAT SINICA. Available: http://dx.doi.org/10.5705/ss.2013.260w.
    Sponsors
    This work was partially supported by NSF grant DMS-1007618, NSF grant EARS-1343155, and Award Number KUS-CI-016-04 made by King Abdullah University of Science and Technology (KAUST). Huang's work was also partially supported by NSF grant DMS-1208952.
    Publisher
    Institute of Statistical Science
    Journal
    Statistica Sinica
    DOI
    10.5705/ss.2013.260w
    ae974a485f413a2113503eed53cd6c53
    10.5705/ss.2013.260w
    Scopus Count
    Collections
    Publications Acknowledging KAUST Support

    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.