Input-response space-filling designs incorporating response uncertainty

IF 2.1 4区 数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Statistical Analysis and Data Mining Pub Date : 2023-11-20 DOI:10.1002/sam.11648
Xiankui Yang, Lu Lu, Christine M. Anderson-Cook
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Abstract

Traditionally space-filling designs have focused on the characteristics of the design in the input space ensuring uniform spread throughout the region. Input-response space-filling designs considered scenarios when having good spread throughout the range or region of the responses is also of interest. This paper acknowledges that there is typically uncertainty associated with the values of the response(s) and hence proposes a method, Input-Response Space-Filling Designs with Uncertainty (IRSFwU), to incorporate this into the design construction. The Pareto front of designs offers alternatives that balance input and response space filling, while prioritizing input combinations with lower associated response uncertainty. These lower uncertainty choices improve the chances of observing the desired response values. We describe the new approach with an uncertainty-adjusted distance to measure the response space filling, the Pareto aggregate point exchange algorithm to populate the set of promising designs, and illustrate the method with three examples of different input and response relationships and dimensions.
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包含响应不确定性的输入-响应空间填充设计
传统的空间填充设计侧重于输入空间的设计特征,确保整个区域的均匀分布。输入-响应空间填充设计考虑了在整个响应范围或区域内具有良好分布的情况,这也是令人感兴趣的。本文承认,通常存在与响应值相关的不确定性,因此提出了一种方法,不确定性输入-响应填充空间设计(IRSFwU),将其纳入设计构造中。Pareto前沿设计提供了平衡输入和响应空间填充的替代方案,同时优先考虑具有较低相关响应不确定性的输入组合。这些不确定性较低的选择提高了观察到所需响应值的机会。我们描述了用不确定性调整距离测量响应空间填充的新方法,用Pareto聚集点交换算法填充有希望的设计集,并通过三个不同输入和响应关系和维度的例子说明了该方法。
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来源期刊
Statistical Analysis and Data Mining
Statistical Analysis and Data Mining COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
3.20
自引率
7.70%
发文量
43
期刊介绍: Statistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact. Of special interest are articles that describe innovative analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce. The focus of the journal is on papers which satisfy one or more of the following criteria: Solve data analysis problems associated with massive, complex datasets Develop innovative statistical approaches, machine learning algorithms, or methods integrating ideas across disciplines, e.g., statistics, computer science, electrical engineering, operation research. Formulate and solve high-impact real-world problems which challenge existing paradigms via new statistical and/or computational models Provide survey to prominent research topics.
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