Spatial subsemble estimator for large geostatistical data
Spatial subsemble estimator for large geostatistical data
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DOI：
10.1016/j.spasta.2017.08.004
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发表时间：
2017-11-01
影响因子：
2.3
通讯作者：
Assuncao, Renato M.
中科院分区：
文献类型：
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作者：
Barbian, Marcia H.;Assuncao, Renato M.
我们介绍了空间子空间的概念，子集集成估计方法在大型空间随机场数据集的分析有用。对完整的数据集进行采样，以给出其参数易于估计的小的空间结构化观测子集;这些子集使用基于其交叉验证预测能力的加权方案进行组合。我们证明了我们的估计是一致的。更重要的是，我们比较的空间substanion与竞争的替代品，并表明我们提出的程序是准确的，比它的竞争对手快得多。我们使用几个来自大型数据集的例子来说明我们的方法的使用。(C)2017爱思唯尔B. V.保留所有权利。
We introduce the concept of spatial subsemble, a subset ensemble estimation method useful in the analysis of large spatial random field datasets. The full dataset is sampled to give small spatially structured subsets of observations whose parameters are easily estimated; these are combined using a weighting scheme based on their cross-validation prediction ability. We show that our estimator is consistent. More importantly, we compare the spatial subsemble with competing alternatives and show that our proposed procedure is both accurate and much faster than its competitor. We illustrate the use of our method using several examples from large datasets. (C) 2017 Elsevier B.V. All rights reserved.