分层模式枚举器及其应用

IF 3.1 1区 数学 Q1 STATISTICS & PROBABILITY
Ye Tian, Hongquan Xu
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引用次数: 0

摘要

摘要空间填充设计在计算机实验中有着广泛的应用。最近提出了一种最小像差型空间填充准则,用于对一系列空间填充设计进行排序和评价,包括基于正交阵列的拉丁超立方体和强正交阵列。然而,由于该准则需要大量的计算来确定空间填充模式,从而衡量各个子区域设计的分层特性,因此难以在实践中应用。在本文中,我们提出了一个分层模式枚举数来表征分层性质。该枚举器易于计算,可以有效地对空间填充设计进行排序。我们证明了分层模式枚举数是空间填充模式的线性组合。基于这种连接,我们开发了计算空间填充模式的高效算法。此外,我们建立了分层模式枚举器的下界,并提出了在伽罗瓦域上使用乘法表实现下界的设计的构造方法。结构设计在低维投影中具有良好的空间填充性能,在各种标准下具有鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stratification pattern enumerator and its applications
Abstract Space-filling designs are widely used in computer experiments. A minimum aberration-type space-filling criterion was recently proposed to rank and assess a family of space-filling designs including orthogonal array-based Latin hypercubes and strong orthogonal arrays. However, it is difficult to apply the criterion in practice because it requires intensive computation for determining the space-filling pattern, which measures the stratification properties of designs on various subregions. In this article, we propose a stratification pattern enumerator to characterise the stratification properties. The enumerator is easy to compute and can efficiently rank space-filling designs. We show that the stratification pattern enumerator is a linear combination of the space-filling pattern. Based on the connection, we develop efficient algorithms for calculating the space-filling pattern. In addition, we establish a lower bound for the stratification pattern enumerator and present construction methods for designs that achieve the lower bound using multiplication tables over Galois fields. The constructed designs have good space-filling properties in low-dimensional projections and are robust under various criteria.
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来源期刊
CiteScore
8.80
自引率
0.00%
发文量
83
审稿时长
>12 weeks
期刊介绍: Series B (Statistical Methodology) aims to publish high quality papers on the methodological aspects of statistics and data science more broadly. The objective of papers should be to contribute to the understanding of statistical methodology and/or to develop and improve statistical methods; any mathematical theory should be directed towards these aims. The kinds of contribution considered include descriptions of new methods of collecting or analysing data, with the underlying theory, an indication of the scope of application and preferably a real example. Also considered are comparisons, critical evaluations and new applications of existing methods, contributions to probability theory which have a clear practical bearing (including the formulation and analysis of stochastic models), statistical computation or simulation where original methodology is involved and original contributions to the foundations of statistical science. Reviews of methodological techniques are also considered. A paper, even if correct and well presented, is likely to be rejected if it only presents straightforward special cases of previously published work, if it is of mathematical interest only, if it is too long in relation to the importance of the new material that it contains or if it is dominated by computations or simulations of a routine nature.
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