论文标题
关于多元空间预测的Cokriging，神经网络和空间盲源分离
On Cokriging, Neural Networks, and Spatial Blind Source Separation for Multivariate Spatial Prediction
论文作者
论文摘要
在不规则采样位置进行的多元测量是一种常见的数据形式，例如在土壤的地球化学分析中。在实际考虑这些测量位置，在未观察到的位置的预测引起了极大的兴趣。对于标准的多元空间预测方法，不仅必须模拟空间依赖性，而且还必须交叉依赖性，这是必不可少的。最近，提出了一种空间数据的盲源分离方法。当使用这种空间盲源分离方法之前，避免了空间交叉依赖性的建模，从而显着简化了空间预测任务。在本文中，我们研究了在广泛的仿真研究中使用空间盲源分离作为空间预测的预处理工具，并将其与Cokriging和dealurnet网络的预测进行了比较。
Multivariate measurements taken at irregularly sampled locations are a common form of data, for example in geochemical analysis of soil. In practical considerations predictions of these measurements at unobserved locations are of great interest. For standard multivariate spatial prediction methods it is mandatory to not only model spatial dependencies but also cross-dependencies which makes it a demanding task. Recently, a blind source separation approach for spatial data was suggested. When using this spatial blind source separation method prior the actual spatial prediction, modelling of spatial cross-dependencies is avoided, which in turn simplifies the spatial prediction task significantly. In this paper we investigate the use of spatial blind source separation as a pre-processing tool for spatial prediction and compare it with predictions from Cokriging and neural networks in an extensive simulation study as well as a geochemical dataset.
