Abstract:
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Efficient designs are in high demand in practice for both computer and physical experiments. Space-filling designs are widely used in both computer and physical experiments. Existing designs may have bad low-dimensional projections, which is undesirable when only a few factors are active. We propose a new design criterion, called uniform projection criterion, by focusing on projection uniformity. We show that the average squared correlation metric is a function of the pairwise L2-distances between the rows only. We further explore the connections amongcolumn-orthogonality, maximin distance and projection uniformity. Based on these connections, we develop new lower and upper bounds for column-orthogonality and projection uniformity from the perspective of distance between design points. These results not only provide new theoretical justifications for each criterion but also help in finding better space-filling designs under multiple criteria. An application of uniform projection designs via a multidrug combination experiment also be given.
个人简介:孙法省,东北师范大学教授、博导,青年长江,吉林省优秀教师。主要从事大数据抽样与分析、计算机试验设计与分析、及高维数据分析等方面的研究。获教育部自然科学二等奖、全国统计科学研究优秀成果奖、吉林省自然科学学术成果奖各一项。
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