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Full-scale approximations of spatio-temporal covariance models for large datasets

Journal Article


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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.

Authors


  •   Zhang, Bohai
  •   Sang, Huiyan (external author)
  •   Huang, Jianhua Z. (external author)

Publication Date


  • 2015

Citation


  • Zhang, B., Sang, H. & Huang, J. Z. (2015). Full-scale approximations of spatio-temporal covariance models for large datasets. Statistica Sinica, 25 99-114.

Scopus Eid


  • 2-s2.0-84955455953

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6032&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5005

Number Of Pages


  • 15

Start Page


  • 99

End Page


  • 114

Volume


  • 25

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.

Authors


  •   Zhang, Bohai
  •   Sang, Huiyan (external author)
  •   Huang, Jianhua Z. (external author)

Publication Date


  • 2015

Citation


  • Zhang, B., Sang, H. & Huang, J. Z. (2015). Full-scale approximations of spatio-temporal covariance models for large datasets. Statistica Sinica, 25 99-114.

Scopus Eid


  • 2-s2.0-84955455953

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6032&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5005

Number Of Pages


  • 15

Start Page


  • 99

End Page


  • 114

Volume


  • 25