FULL-SCALE APPROXIMATIONS OF SPATIO-TEMPORAL COVARIANCE MODELS FOR LARGE DATASETS Academic Article

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

• Sang, Huiyan

published proceedings

• STATISTICA SINICA

author list (cited authors)

• Zhang, B., Sang, H., & Huang, J. Z.

citation count

• 30

complete list of authors

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

publication date

• January 2015

publisher

• Statistica Sinica (Institute of Statistical Science)  Publisher

published in

• STATISTICA SINICA  Journal

keywords

• Covariance Approximation
• Gaussian Process
• Knot Selection
• Reversible Jump Markov Chain Monte Carlo
• Sparse Matrix

Digital Object Identifier (DOI)

• 10.5705/ss.2013.260w

start page

• 99

end page

• 114

volume

• 25

issue

• 1