On the maximin distance properties of orthogonal designs via the rotation  

作　　者：Yaping Wang Fasheng Sun 

机构地区：[1]Key Laboratory of Advanced Theory and Application in Statistics and Data Science,Ministry of Education,School of Statistics,East China Normal University,Shanghai 200062,China [2]Key Laboratory for Applied Statistics of Ministry of Education,School of Mathematics and Statistics,Northeast Normal University,Changchun 130024,China

出　　处：《Science China Mathematics》2023年第7期1593-1608,共16页中国科学：数学（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.11901199 and 71931004),supported by National Natural Science Foundation of China(Grant Nos.11971098 and 11471069);the Open Research Fund of Key Laboratory for Applied Statistics of Ministry of Education,Northeast Normal University(Grant No.130028906);Shanghai Chenguang Program(Grant No.19CG26);National Key R&D Program of China(Grant No.2020YFA0714102)。

摘　　要：Space-filling designs are widely used in computer experiments.They are frequently evaluated by the orthogonality and distance-related criteria.Rotating orthogonal arrays is an appealing approach to constructing orthogonal space-filling designs.An important issue that has been rarely addressed in the literature is the design selection for the initial orthogonal arrays.This paper studies the maximin L_(2)-distance properties of orthogonal designs generated by rotating two-level orthogonal arrays under three criteria.We provide theoretical justifications for the rotation method from a maximin distance perspective and further propose to select initial orthogonal arrays by the minimum G_(2)-aberration criterion.New infinite families of orthogonal or 3-orthogonal U-type designs,which also perform well under the maximin distance criterion,are obtained and tabulated.Examples are presented to show the effectiveness of the constructed designs for building statistical surrogate models.

关 键 词：computer experiment Latin hypercube design minimum G_(2)-aberration space-filling design U-type design 

分 类 号：O212.6[理学—概率论与数理统计]