论文标题
大约最佳的空间设计：它有多好？
Approximately Optimal Spatial Design: How Good is it?
论文作者
论文摘要
对不利人类健康状况与许多环境物质以及过程之间关联的认识日益认识，导致需要监测它们。环境统计数据中出现的一个重要问题是监测站的位置设计为这些感兴趣的环境过程。一种用于监视网络的特定设计标准试图减少关于看不见过程的预测的不确定性，称为最大渗透设计。但是，该设计标准涉及一个硬优化问题，该问题在大型数据集上在计算上很棘手。 Wang等人的先前工作。 （2017年）检查了一个概率模型，该模型可以有效地实施以近似基础优化问题。在本文中，我们试图建立用于评估近似质量的统计声音工具。
The increasing recognition of the association between adverse human health conditions and many environmental substances as well as processes has led to the need to monitor them. An important problem that arises in environmental statistics is the design of the locations of the monitoring stations for those environmental processes of interest. One particular design criterion for monitoring networks that tries to reduce the uncertainty about predictions of unseen processes is called the maximum-entropy design. However, this design criterion involves a hard optimization problem that is computationally intractable for large data sets. Previous work of Wang et al. (2017) examined a probabilistic model that can be implemented efficiently to approximate the underlying optimization problem. In this paper, we attempt to establish statistically sound tools for assessing the quality of the approximations.
