Testing the covariance structure of multivariate random fields
Testing the covariance structure of multivariate random fields
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DOI：
10.1093/biomet/asn053
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发表时间：
2008-12
期刊：
影响因子：
2.7
通讯作者：
Bo Li;M. Genton;M. Sherman
中科院分区：
文献类型：
--
作者：
Bo Li;M. Genton;M. Sherman
多元空间数据和多元时空数据的出现日益丰富。对于这些数据，建模的一个重要部分是评估描述变量、空间和时间相关性的基本协方差函数的属性。在本文中，我们提出了一种方法来评估适当的几种类型的常见假设的多元协方差函数的时空背景。该方法是基于样本时空互协方差估计量的渐近联合正态性。具体来说，我们解决的对称性，可分性和线性模型coregionalization的假设。我们进行模拟实验，以评估我们的测试的大小和权力，并说明我们的方法在加州污染物的三变量时空数据集。版权所有2008年，牛津大学出版社。
There is an increasing wealth of multivariate spatial and multivariate spatio-temporal data appearing. For such data, an important part of model building is an assessment of the properties of the underlying covariance function describing variable, spatial and temporal correlations. In this paper, we propose a methodology to evaluate the appropriateness of several types of common assumptions on multivariate covariance functions in the spatio-temporal context. The methodology is based on the asymptotic joint normality of the sample space-time cross-covariance estimators. Specifically, we address the assumptions of symmetry, separability and linear models of coregionalization. We conduct simulation experiments to evaluate the sizes and powers of our tests and illustrate our methodology on a trivariate spatio-temporal dataset of pollutants over California. Copyright 2008, Oxford University Press.