Rapid numerical approximation method for integrated covariance functions over irregular data regions
Rapid numerical approximation method for integrated covariance functions over irregular data regions
复制标题
不规则数据区域积分协方差函数的快速数值逼近方法
DOI：
10.1002/sta4.275
复制
发表时间：
2020
期刊：
影响因子：
1.7
通讯作者：
Bandyopadhyay, Soutir
中科院分区：
文献类型：
--
作者：
Simonson, Peter;Nychka, Douglas;Bandyopadhyay, Soutir
在许多实际应用中，空间数据通常是在区域级别（即块数据）收集的，并且在与观察到的点或块不同的点或块上对变量的推断和预测通常取决于底层连续空间过程的积分。在本文中，我们描述了一种基于傅立叶变换的方法，通过该方法可以以与传统方法相同的精度水平对不规则数据区域上的协方差函数的多重积分进行数值近似，但大大减少了计算费用。
In many practical applications, spatial data are often collected at areal levels (i.e., block data), and the inferences and predictions about the variable at points or blocks different from those at which it has been observed typically depend on integrals of the underlying continuous spatial process. In this paper, we describe a method based onFourier transformsby which multiple integrals of covariance functions over irregular data regions may be numerically approximated with the same level of accuracy as traditional methods, but at a greatly reduced computational expense.
不规则空间区域上平均过程的协方差特性的近似
影响因子：
0.8
作者：
R. J. Martin;J. Dwyer
通讯作者：
J. Dwyer