Cluster sampling for Morris method made easy
Cluster sampling for Morris method made easy
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DOI：
10.1002/nav.21968
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Wen Shi;Xi Chen
中科院分区：
文献类型：
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作者：
Wen Shi;Xi Chen
在本文中，我们提供了一个彻底的调查莫里斯的基本效应方法（MM），一个流行的无模型因素筛选方法起源于设置的设计和分析的计算实验的整群抽样方案。我们首先研究支持MM的两个采样方案的采样机制（即，整群抽样和非整群抽样），并揭示其性质作为一个两级嵌套抽样过程。这种深入的理解为解决集群抽样的两个重要方面奠定了基础：预算分配和抽样计划。一方面，我们在方差分析框架下研究了整群抽样的预算分配问题，得到了有效估计重要性测度的最优预算分配。另一方面，我们设计了一个有效的聚类抽样算法，两个变量，以实现增强的统计特性。数值评估表明，所提出的聚类抽样算法和预算分配的优越性，得出（当单独使用和结合使用）现有的集群和非集群抽样计划。
In this paper we provide a thorough investigation of the cluster sampling scheme for Morris' elementary effects method (MM), a popular model‐free factor screening method originated in the setting of design and analysis of computational experiments. We first study the sampling mechanism underpinning the two sampling schemes of MM (i.e., cluster sampling and noncluster sampling) and unveil its nature as a two‐level nested sampling process. This in‐depth understanding sets up a foundation for tackling two important aspects of cluster sampling: budget allocation and sampling plan. On the one hand, we study the budget allocation problem for cluster sampling under the analysis of variance framework and derive optimal budget allocations for efficient estimation of the importance measures. On the other hand, we devise an efficient cluster sampling algorithm with two variants to achieve enhanced statistical properties. The numerical evaluations demonstrate the superiority of the proposed cluster sampling algorithm and the budget allocations derived (when used both separately and in conjunction) to existing cluster and noncluster sampling schemes.