A BAYESIAN SPATIO-TEMPORAL GEOSTATISTICAL MODEL WITH AN AUXILIARY LATTICE FOR LARGE DATASETS
A BAYESIAN SPATIO-TEMPORAL GEOSTATISTICAL MODEL WITH AN AUXILIARY LATTICE FOR LARGE DATASETS
复制标题
大数据集辅助格的贝叶斯时空地统计模型
DOI：
10.5705/ss.2013.085w
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发表时间：
2014
影响因子：
1.4
通讯作者：
M. Genton
中科院分区：
文献类型：
--
作者：
Ganggang Xu;F. Liang;M. Genton
当时空数据集很大时，计算负担可能导致传统地统计工具的实施失败。本文提出了一种计算效率高的贝叶斯分层时空模型，其中空间相关性用高斯马尔可夫随机场（GMRF）近似，时间相关性用向量自回归模型描述。通过在感兴趣的空间区域上引入一个辅助网格，该方法不仅能够处理空间域中的不规则间隔观测，而且能够绕过时空过程中的数据缺失问题。由于所提出的马尔可夫链蒙特卡罗算法的计算复杂度是O（n）的顺序与n在空间和时间的观测总数，我们的方法可以用来处理非常大的时空数据集与合理的CPU时间。使用模拟研究和来自美国的降水数据集来说明所提出的模型的性能。
When spatio-temporal datasets are large, the computational burden can lead to failures in the implementation of traditional geostatistical tools. In this pa- per, 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 observa- tions 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 pro- posed model is illustrated using simulation studies and a dataset of precipitation data from the coterminous United States.