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Spatial interpolation of environmental parameters with Copulas

使用 copula 进行环境参数的空间插值

基本信息

批准号：
36474518
负责人：
Professor Dr.-Ing. András Bárdossy
金额：
--
依托单位：
Lehrstuhl für Hydrologie und Geohydrologie
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2007
资助国家：
德国
起止时间：
2006-12-31 至 2010-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/36474518?language=en
关键词：
Spatial interpolation environmental parameters Copulas

项目摘要

In the first phase of this project it has been shown that Copulas offer a great novel tool to model nongaussian dependence structures for spatially distributed parameters. Magnitude- and geometry-dependent Copula based models have successfully been applied for interpolation purposes. Copulas have started to be acknowledged by the international research community and are a key topic at the forefront of geostatistical research (Section 2).Using Copulas for interpolation purposes allows for an improved uncertainty estimate of the interpolation due to the fact that a full distribution function of the estimate can be calculated. These uncertainty estimates were validated using a split-sampling approach. An improved uncertainty estimate can prove to be beneficial for risk assessment. The possibility to express heterogeneous uncertainty can be important for value-dependent observation strategies, for example in observation network design.Within the second phase of this project it is proposed to advance Copula based models in four areas: (1) to accommodate inequality constraints for interpolation which will allow to take discrete-continuous data into account, (2) to incorporate prior information to improve interpolation both for numerical and categorical data types, (3) to find new types of theoretical Copulas to improve the fit to real-world data-sets, and (4) to find a conditional simulation technique.

在这个项目的第一阶段，它已被证明，Copula提供了一个伟大的新的工具来模拟非高斯依赖结构的空间分布参数。幅度和几何依赖的Copula模型已成功地应用于插值目的。Copula函数已开始得到国际研究界的认可，并且是地统计学研究前沿的一个关键主题（第2节）。使用Copula函数进行插值可以改善插值的不确定性估计，因为可以计算估计的完整分布函数。这些不确定性估计值使用分裂抽样方法进行验证。一个改进的不确定性估计可以证明是有益的风险评估。表达异质不确定性的可能性对于值依赖的观测策略可能是重要的，例如在观测网络设计中。在该项目的第二阶段，建议在四个领域推进基于Copula的模型：（1）为了适应插值的不等式约束，这将允许考虑离散-连续数据，（2）结合先验信息以改进数值和分类数据类型的插值，（3）找到新类型的理论Copula以改进对真实世界数据集的拟合，以及（4）找到条件模拟技术。