{"title":"Input-response space-filling designs incorporating response uncertainty","authors":"Xiankui Yang, Lu Lu, Christine M. Anderson-Cook","doi":"10.1002/sam.11648","DOIUrl":null,"url":null,"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.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"16 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Analysis and Data Mining","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1002/sam.11648","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
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.
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.