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Coordination of spatially balanced samples

机译：空间平衡样本的协调

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摘要

Sample coordination seeks to create a probabilistic dependence between the selection of two or more samples drawn from the same population or from overlapping populations. Positive coordination increases the expected sample overlap, while negative coordination decreases it. There are numerous applications for sample coordination with varying objectives. A spatially balanced sample is a sample that is well-spread in some space. Forcing a spread within the selected samples is a general and very efficient variance reduction technique for the Horvitz-Thompson estimator. The local pivotal method and the spatially correlated Poisson sampling are two general schemes for achieving well-spread samples. We aim to introduce coordination for these sampling methods based on the concept of permanent random numbers. The goal is to coordinate such samples while preserving spatial balance. The proposed methods are motivated by examples from forestry, environmental studies, and official statistics.
机译：样本协调试图在从相同总体或重叠总体中抽取两个或多个样本之间建立概率依赖性。正协调会增加预期的样本重叠，而负协调会减少预期的重叠。具有不同目标的样品协调有许多应用。空间平衡样本是在某些空间中广泛分布的样本。对于Horvitz-Thompson估计器，在选定样本中强制分布是一种通用且非常有效的方差减少技术。局部枢轴方法和空间相关的泊松采样是实现样本扩展的两种通用方案。我们旨在基于永久随机数的概念为这些采样方法引入协调。目的是在保持空间平衡的同时协调此类样本。所提出的方法以林业，环境研究和官方统计为例。

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