Infilling sparse records of spatial fields
Infilling sparse records of spatial fields
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DOI：
10.1198/016214503000000729
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发表时间：
2003-12-01
影响因子：
3.7
通讯作者：
Daly, C
中科院分区：
文献类型：
--
作者：
Johns, CJ;Nychka, D;Daly, C
天气的历史记录，例如上个世纪的每月降水量和气温，是研究气候变化和变异性的宝贵数据库。这些数据还为理解和模拟气候、生态过程和人类活动之间的关系提供了起点。然而，这些数据在空间和时间上的观察是不规则的。基本的统计问题是创建与观察到的数据一致且对其他科学学科有用的完整数据记录。我们修改高斯反转 Wishart 空间场模型以适应不规则的数据模式并促进计算。我们实现的新颖功能包括使用交叉验证来确定给予回归和地质统计组件的相对先验权重，以及使用空间填充子集来减少某些参数的计算。我们认为整体方法是有优点的，在计算可行性和统计有效性之间划定了界限。此外，我们能够对估计的不确定性进行可靠的测量。
Historical records of weather, such as monthly precipitation and temperatures from the last century, are an invaluable database to use in studying changes and variability in climate. These data also provide the starting point for understanding and modeling the relationship among climate, ecological processes, and human activities. However, these data are observed irregularly over space and time. The basic statistical problem is to create a complete data record that is consistent with the observed data and is useful for other scientific disciplines. We modify the Gaussian-inverted Wishart spatial field model to accommodate irregular data patterns and to facilitate computations. Novel features of our implementation include the use of cross-validation to determine the relative prior weight given to the regression and geostatistical components and the use of a space-filling subset to reduce the computations for some parameters. We feel that the overall approach has merit, treading a line between computational feasibility and statistical validity. Furthermore, we are able to produce reliable measures of uncertainty for the estimates.