A spatio-temporal blockwise Euclidean likelihood method for the estimation of covariance models when dealing with large spatio-temporal Gaussian data is proposed. The method uses moment conditions coming from the score of the pairwise composite likelihood. The blockwise approach guarantees considerable computational improvements over the standard pairwise composite likelihood method. In order to further speed up computation, a general purpose graphics processing unit implementation using OpenCL is implemented. The asymptotic properties of the proposed estimator are derived and the finite sample properties of this methodology by means of a simulation study highlighting the computational gains of the OpenCL graphics processing unit implementation. Finally, there is an application of the estimation method to a wind component data set.

提出了一种处理大时空高斯数据时协方差模型估计的时空分块欧几里德似然方法。该方法使用来自成对复合似然得分的矩条件。与标准的成对复合似然方法相比，逐块方法保证了相当大的计算改进。为了进一步加快计算速度，实现了使用 OpenCL 的通用图形处理单元实现。通过强调 OpenCL 图形处理单元实现的计算增益的模拟研究，推导出了所提出的估计器的渐近特性和该方法的有限样本特性。最后，将估计方法应用于风分量数据集。