Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets Academic Article uri icon

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.

author list (cited authors)

  • Zhang, B., Sang, H., & Huang, J. Z

complete list of authors

  • Zhang, Bohai||Sang, Huiyan||Huang, Jianhua Z

publication date

  • January 1, 2014 11:11 AM