A Bayesian spatio-temporal geostatistical model with an auxiliary lattice for large datasets
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionSpatio-Temporal Statistics and Data Analysis Group
Statistics Program
Date
2014-01-09Online Publication Date
2014-01-09Print Publication Date
2014Permanent link to this record
http://hdl.handle.net/10754/550847
Metadata
Show full item recordAbstract
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 SINICAPublisher
Institute of Statistical ScienceJournal
STATISTICA SINICAAdditional Links
http://www3.stat.sinica.edu.tw/statistica/J25N1/J25N14/J25N14.htmlae974a485f413a2113503eed53cd6c53
10.5705/ss.2013.085w