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Study of Geostatistical Predictions based on Block Data

基于块数据的地统计预测研究

基本信息

批准号：
18500211
负责人：
MASE Shigeru
金额：
$ 1.52万
依托单位：
Tokyo Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2006
资助国家：
日本
起止时间：
2006 至 2007
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18500211/
关键词：
Block Data Geostatistics Spatial Prediction Maximu Likelihood Estimator Cokriging Numerical Integration Statistical Analysis System R Programming Manual クリギング法画像修復

项目摘要

In the master thesis "On the study of an algorithm of parameter estimation for Kriging method for block data" (2006, Koji Takahashi), we proposed a Kriging method for geostatistical prediction based on block data and developed an efficient algorithm for calculating maximum likelihood estimators of Kriging. In this study, block are restricted to congruent rectangles. We have been trying to extend the proposed algorithm to block of more general forms. But this needs calculation of a lot of large covariance matrices Each elements of which needs a time-consuming numerical integrations and distributed computations are unavoidable. We have been carrying numerical experiments and hope the result will be published in near future.In the master thesis "On the study of cokrging method when there are numerous covariates" (2007, Kenji Usuda), we consider a cokriging method under the condition that there are numerous covariates. In this situation, a standard method uses only a part of covariates in order to ease numerical computations and certainly discards a certain information contained covariates. We proposed to use blocks into which cavariates are partitioned and use covariances between blocks. A preliminary numerical experiments done in the thesis, our new method shows a better performance than a traditional method. We hope to publish the results after more experiments in near future.Also Mase published a programming manual of the system R which is an open source statistical system which has become a common workbench of statistical analysis all over the world. The book has gotten a good reputation.

在硕士论文“关于块数据克里金法参数估计算法的研究”（2006，高桥浩二）中，我们提出了一种基于块数据的地统计预测克里金方法，并开发了一种计算克里金最大似然估计量的有效算法。在本研究中，块仅限于全等矩形。我们一直在尝试将所提出的算法扩展到更通用形式的块。但这需要计算大量大型协方差矩阵，其中每个元素都需要耗时的数值积分，分布式计算是不可避免的。我们一直在进行数值实验，希望在不久的将来能够发表结果。在硕士论文《论存在大量协变量时的协同克里金法的研究》（2007，Kenji Usuda）中，我们考虑了存在大量协变量情况下的协同克里金法。在这种情况下，标准方法仅使用一部分协变量以简化数值计算，并且肯定会丢弃协变量中包含的某些信息。我们建议使用将cavariate划分成的块并使用块之间的协方差。论文中进行的初步数值实验表明，我们的新方法比传统方法具有更好的性能。我们希望在不久的将来经过更多的实验后公布结果。Mase还出版了R系统的编程手册，R系统是一个开源统计系统，已成为全世界统计分析的通用工作平台。这本书获得了良好的声誉。