A Fast, Optimal Spatial-Prediction Method for Massive Datasets
A Fast, Optimal Spatial-Prediction Method for Massive Datasets
复制标题
一种针对海量数据集的快速、最优空间预测方法
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
N. Cressie
中科院分区：
文献类型：
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作者：
S. Tzeng;Hsin;N. Cressie
本文考虑了一类彼此空间移位的多分辨率树状结构模型，并提出了一种新的空间预测方法，该方法对这类模型的成员产生的最佳空间预测因子进行平均。因此，即使从单个多分辨率树结构模型单独生成的预测器不是光滑的，最终预测的表面也是光滑的。我们将这种新的预测器称为多分辨率空间（MURS）预测器，并为此开发了一种计算效率高的算法。该算法可以处理大量的数据集，即使有些观测值缺失。此外，MURS预测器可以被证明是一大类协方差函数的最小均方误差预测器。大规模数据集的仿真实例表明，MURS方法始终优于两种常用的滤波方法。利用新方法对卫星遥感总臭氧数据进行了分析。
This article considers a class of multiresolution tree-structured models that are spatially shifted versions of each other and proposes a new spatial-prediction method that averages over the optimal spatial predictors produced from members of this class of models. As a consequence, the resulting predicted surface is smooth, even when the predictors generated separately from individual multiresolution tree-structured models are not. We call the new predictor the multiresolution spatial (MURS) predictor and develop a computationally efficient algorithm for it. The algorithm can handle massive datasets even when some observations are missing. Moreover, the MURS predictor can be shown to be the minimum mean squared error predictor for a large class of covariance functions. A simulation example for massive datasets shows that the MURS method consistently outperforms two commonly used filtering methods. Total column ozone data remotely sensed from a satellite are analyzed using the new methodology.