{"title":"构建高维高分离距离设计","authors":"Xu He , Fasheng Sun","doi":"10.1016/j.jspi.2024.106150","DOIUrl":null,"url":null,"abstract":"<div><p>Space-filling designs that possess high separation distance are useful for computer experiments. We propose a novel method to construct high-dimensional high-separation distance designs. The construction involves taking the Kronecker product of sub-Hadamard matrices and rotation. In addition to possessing better separation distance than most existing types of space-filling designs, our newly proposed designs enjoy orthogonality and projection uniformity and are more flexible in the numbers of runs and factors than that from most algebraic constructions. From numerical results, such designs are excellent in Gaussian process emulation of high-dimensional computer experiments. An R package on design construction is available online.</p></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"232 ","pages":"Article 106150"},"PeriodicalIF":0.8000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Construction of high-dimensional high-separation distance designs\",\"authors\":\"Xu He , Fasheng Sun\",\"doi\":\"10.1016/j.jspi.2024.106150\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Space-filling designs that possess high separation distance are useful for computer experiments. We propose a novel method to construct high-dimensional high-separation distance designs. The construction involves taking the Kronecker product of sub-Hadamard matrices and rotation. In addition to possessing better separation distance than most existing types of space-filling designs, our newly proposed designs enjoy orthogonality and projection uniformity and are more flexible in the numbers of runs and factors than that from most algebraic constructions. From numerical results, such designs are excellent in Gaussian process emulation of high-dimensional computer experiments. An R package on design construction is available online.</p></div>\",\"PeriodicalId\":50039,\"journal\":{\"name\":\"Journal of Statistical Planning and Inference\",\"volume\":\"232 \",\"pages\":\"Article 106150\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2024-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Statistical Planning and Inference\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378375824000077\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistical Planning and Inference","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378375824000077","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
摘要
具有高分离距离的空间填充设计对计算机实验非常有用。我们提出了一种构建高维高分离距离设计的新方法。这种构建方法涉及子哈达玛矩阵的克朗内克乘积和旋转。与大多数现有的空间填充设计相比,我们新提出的设计除了具有更好的分离距离外,还具有正交性和投影均匀性,并且在运行数和因子数方面比大多数代数构造更加灵活。从数值结果来看,这种设计在高维计算机实验的高斯过程仿真中表现出色。有关设计构造的 R 软件包可在线获取。
Construction of high-dimensional high-separation distance designs
Space-filling designs that possess high separation distance are useful for computer experiments. We propose a novel method to construct high-dimensional high-separation distance designs. The construction involves taking the Kronecker product of sub-Hadamard matrices and rotation. In addition to possessing better separation distance than most existing types of space-filling designs, our newly proposed designs enjoy orthogonality and projection uniformity and are more flexible in the numbers of runs and factors than that from most algebraic constructions. From numerical results, such designs are excellent in Gaussian process emulation of high-dimensional computer experiments. An R package on design construction is available online.
期刊介绍:
The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject. While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists.
We publish high quality articles in all branches of statistics, probability, discrete mathematics, machine learning, and bioinformatics. We also especially welcome well written and up to date review articles on fundamental themes of statistics, probability, machine learning, and general biostatistics. Thoughtful letters to the editors, interesting problems in need of a solution, and short notes carrying an element of elegance or beauty are equally welcome.