A Projection Space-Filling Criterion and Related Optimality Results
投影空间填充准则及相关最优性结果AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要研究计算机实验的空间填充设计与通用最优性理论，没有足够文本依据对应所列计量领域细分。
反馈分类问题正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
投影空间填充准则及相关最优性结果
- 研究问题
- 如何在评价计算机实验设计的空间填充性时，同时考虑投影子区域的大小与维度，并据此寻找最优设计？
- 方法与思路
- 作者扩展空间填充层级原则，提出按相同大小子区域的投影维度由低到高逐次比较设计的准则，并研究其最优构造。
- 材料陈述的贡献
- 摘要称，新准则有助于选择用于统计代理模型的设计，并给出理论最优性结果，刻画若干最优的三阶强正交阵类别。
- 阅读关注点
- 应核对空间填充性的具体定义、逐次排序规则与既有聚合准则的差别，以及最优性结果覆盖的设计类别和模拟设置。
- 阅读边界
- 仅据OpenAlex索引摘要，稿件版本未注明；最优性条件与模拟数值未提供，不能推断大规模表现或后续刊本变化。
原文摘要
Computer experiments call for space-filling designs. Recently, a minimum aberration type space-filling criterion was proposed to rank and assess a family of space-filling designs including Latin hypercubes and strong orthogonal arrays. It aims at capturing the space-filling properties of a design when projected onto subregions of various sizes. In this article, we also consider the dimension aside from the sizes of subregions by proposing first an expanded space-filling hierarchy principle and then a projection space-filling criterion as per the new principle. When projected onto subregions of the specific size, the proposed criterion ranks designs via sequentially maximizing the space-filling properties on equally sized subregions in lower dimensions to higher dimensions, while the minimum aberration type space-filling criterion compares designs by maximizing the aggregate space-filling properties on multidimensional subregions of the same size. We present illustrative examples to demonstrate two criteria and conduct simulations as evidence of the utility of our criterion in terms of selecting efficient space-filling designs to build statistical surrogate models. We further consider the construction of the optimal space-filling designs under the proposed criterion. Although many algorithms have been proposed for generating space-filling designs, it is well-known that they often deteriorate rapidly in performance for large designs. In this article, we develop some theoretical optimality results and characterize several classes of strong orthogonal arrays of strength three that are the most space-filling. Supplementary materials for this article are available online.