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    A Bayesian spatio-temporal geostatistical model with an auxiliary lattice for large datasets

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    Type
    Article
    Authors
    Xu, Ganggang
    Liang, Faming
    Genton, Marc G. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Spatio-Temporal Statistics and Data Analysis Group
    Statistics Program
    Date
    2014-01-09
    Online Publication Date
    2014-01-09
    Print Publication Date
    2014
    Permanent link to this record
    http://hdl.handle.net/10754/550847
    
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    Abstract
    When spatio-temporal datasets are large, the computational burden can lead to failures in the implementation of traditional geostatistical tools. In this paper, we propose a computationally efficient Bayesian hierarchical spatio-temporal model in which the spatial dependence is approximated by a Gaussian Markov random field (GMRF) while the temporal correlation is described using a vector autoregressive model. By introducing an auxiliary lattice on the spatial region of interest, the proposed method is not only able to handle irregularly spaced observations in the spatial domain, but it is also able to bypass the missing data problem in a spatio-temporal process. Because the computational complexity of the proposed Markov chain Monte Carlo algorithm is of the order O(n) with n the total number of observations in space and time, our method can be used to handle very large spatio-temporal datasets with reasonable CPU times. The performance of the proposed model is illustrated using simulation studies and a dataset of precipitation data from the coterminous United States.
    Citation
    A Bayesian spatio-temporal geostatistical model with an auxiliary lattice for large datasets 2014 STATISTICA SINICA
    Publisher
    Institute of Statistical Science
    Journal
    STATISTICA SINICA
    DOI
    10.5705/ss.2013.085w
    Additional Links
    http://www3.stat.sinica.edu.tw/statistica/J25N1/J25N14/J25N14.html
    ae974a485f413a2113503eed53cd6c53
    10.5705/ss.2013.085w
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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