强度3的混合正交表的构造

对称正交表和混合正交表不仅在试验设计中有着重要的应用价值,而且它们也是构造其他组合构形的强有力工具.本文首先讨论了强度为3的差阵,得到了一些差阵的新结果,并且利用差阵和Hadamard矩阵给出了强度3的混合正交表的新的构造方法.作为应用,本文得到了一批新的强度3的混合正交表,并且有一部分是紧的.     

标准混合差集矩阵的构造

通过利用差集矩阵和投影矩阵的正交分解之间的关系,首先提出了构造小的标准混合差集矩阵的一般方法.其次,给定一个阶为r+1的标准混合差集矩阵和一个阶为r的差集矩阵,首先提出了构造阶为r(r+1)的标准混合差集矩阵的一般方法.如果阶为r的差集矩阵不存在但一个试验次数为r~2的正交表存在,也可以通过它们构造阶数较大的标准混合差集矩阵.     

广义正交表的加法构造

正交表的构造技术中有一种加法构造,那么广义正交表的构造是否可以借鉴这种方法呢?对广义正交表构造也采用类似的方法,研究发现,在两个广义正交表的基础上,进行列重叠、列取模等简单替换,可以构造许多新的广义正交表,其加法构造方法比正交表的加法构造方法更加简单,并且若原有的两个广义正交表是饱和的,那么在此基础上新构造的广义正交表也是饱和的.     

构造正交表的分层方法

利用投影矩阵的正交分解提出了构造正交表的分层方法,作为这种方法的应用构造了一个含有9水平的36阶正交表。     

一类正交投影矩阵及其相关正交表

本文给出了一类正交投影矩阵及其相关的强度2正交表.使用这些正交投影矩阵和正交表,我们提供了一种构造正交表的方法,并且构造了一些混合水平正交表.     

平衡区组正交表的分列和并列技术

平衡区组正交表的构造类似于正交表的构造.例如:正交表构造理论中有一个常用的分列和并列技术,这种技术能否推广到平衡区组正交表的构造理论之中呢?本文探讨了用某些已知低水平的设计表替换平衡区组正交表的高水平列(分列技术),或者已知的平衡区组正交表的多个低水平列,合并成一个高水平列(并列技术).研究发现:用正交表作为桥梁,可以进行平衡区组正交表的分列和并列构造.不但从理论上证明了结论,而且用算例分析验证了此构造方法的有效性.     

关于单一自由度比较分析法的注

如果用通常方差分析的方法,检验多个正态总体均值有显著差异,文[1]以实例介绍了单一自由度比较均值的方法,值得推广应用。该分析法关键是设计单一自由度独立而正确的比较表.而要设计这样的比较表,除了选择有实际意义的比较内容外,在文[1]中正确构造正交系数表是很关键的──要利用正交系数表来判断设计是否为单一自由度独立而正确的比较。正交系数表的构造虽有一定的原则可循,但构造和验证正交系统表并不容易,而且这些正交系数表总共有多少还是个问题。本文提出可不用正交系数表来设计(多水平的)单一自由度所有独立而正确的比较表的简单方法…     

对奇素数p构造一类生成正交阵列(2p~2,2p 1,p,2)的差集

自从正交设计在工农业生产的科学实验中普及推广以来,许多单位和个人对正交阵列即正交表的构造颇感兴趣。其中尤其引起人们注意的是正交表L_(18)(3~7)和L_(50)(5~(11))等的构造。构造这类正交表,确实相当复杂,有的用代数方法,例如Addelman-Kempthorne;有的用数论方法,例如Masuyama,而且具体地编造一个表,还需要做大量的计算。     

一类9n2次组合混合水平正交表的构造

本文利用正交表和投影矩阵的正交分解之间的关系,给出了一类9n2次组合混合水平正交表的构造方法,作为这种方法的应用,我们构造了一些新的具有较大非素数幂水平的144次混合水平正交表,并且这些正交表具有较高的饱和率.     

基于正交表替换构造广义正交表

广义正交表是一种类似于正交表的新设计,利用广义正交表进行试验设计,在与正交表具有相同估计方差的条件下,可以明显减少试验次数.对广义正交表的构造方法进行深入研究,研究发现,用正交表和已知的小试验次数的广义正交表,经过简单替换,可以构造许多新的广义正交表,并且新构造的广义正交表还保持着原来正交表列之间的正交性.     

Orthogonal arrays obtained by generalized difference matrices with g levels

Nowadays orthogonal arrays play important roles in statistics, computer science, coding theory and cryptography. The usual difference matrices are essential for the construction for many mixed orthogonal arrays. But there are also orthogonal arrays which cannot be obtained by the usual difference matrices, such as mixed orthogonal arrays of run size 60. In order to construct these mixed orthogonal arrays, a class of special so-called generalized difference matrices were discovered by Zhang (1989,1990,1993,2...     

The Existence of Mixed Orthogonal Arrays with Four and Five Factors of Strength Two

Symmetric orthogonal arrays and mixed orthogonal arrays are useful in the design of various experiments. They are also a fundamental tool in the construction of various combinatorial configurations. In this paper, we investigated the mixed orthogonal arrays with four and five factors of strength two, and proved that the necessary conditions of such mixed orthogonal arrays are also sufficient with several exceptions and one possible exception.     

A Family of Asymmetrical Orthogonal Arrays with Run Sizes 4p^2

Nowadays orthogonal arrays play important roles in statistics, computer science, coding theory and cryptography. The usual difference matrices are essential for the construction of many mixed orthogonal arrays. But there are also many orthogonal arrays, especially mixed-level or asymmetrical which can not be obtained by the usual difference matrices. In order to construct these asymmetrical orthogonal arrays, a class of special matrices, so-called generalized difference matrices, were discovered by Zhang（1989, 1990, 1993） by the orthogonal decompositions of projective matrices. In this article, an interesting equivalent relationship between the orthogonal arrays and the generalized difference matrices is presented. As an application, a family of orthogonal arrays of run sizes 4p2, such as L36（6^13^42^10）, are constructed.     

一类试验次数为4p2的非对称正交表的构造

Nowadays orthogonal arrays play important roles in statistics,computer science, coding theory and cryptography.The usual difference matrices are essential for the con- struction of many mixed orthogonal arrays.But there are also many orthogonal arrays, especially mixed-level or asymmetrical which can not be obtained by the usual difference matrices.In order to construct these asymmetrical orthogonal arrays,a class of special matrices,so-called generalized difference matrices,were discovered by Zhang(1989,1990, 1993) by the orthogonal decompositions of projective matrices.In this article,an interesting equivalent relationship between the orthogonal arrays and the generalized difference matri- ces is presented.As an application,a family of orthogonal arrays of run sizes 4p~2,such as L_(36)(6~13~42~(10)),are constructed.     

Parameter Inequalities for Orthogonal Arrays with Mixed Levels

An important question in the construction of orthogonal arrays is what the minimal size of an array is when all other parameters are fixed. In this paper, we will provide a generalization of an inequality developed by Bierbrauer for symmetric orthogonal arrays. We will utilize his algebraic approach to provide an analogous inequality for orthogonal arrays having mixed levels and show that the bound obtained in this fashion is often sharper than Raos bounds. We will also provide a new proof of Raos inequalities for arbitrary orthogonal arrays with mixed levels based on the same method.     

Optimal and near-optimal mixed covering arrays by column expansion

This paper describes constructions for strength-2 mixed covering arrays developed from index-1 orthogonal arrays, ordered designs and covering arrays. The constructed arrays have optimal or near-optimal sizes. Conditions for achieving optimal size are described. An optimization among the different ingredient arrays to maximize the number of factors of each alphabet size is also presented.     

Generalized latin matrix and construction of orthogonal arrays

In this paper, generalized Latin matrix and orthogonal generalized Latin matrices are proposed. By using the property of orthogonal array, some methods for checking orthogonal generalized Latin matrices are presented. We study the relation between orthogonal array and orthogonal generalized Latin matrices and obtain some useful theorems for their construction. An example is given to illustrate applications of main theorems and a new class of mixed orthogonal arrays are obtained.     

On finding mixed orthogonal arrays of strength 2 with many 2-level factors

We describe a method for finding mixed orthogonal arrays of strength 2 with a large number of 2-level factors. The method starts with an orthogonal array of strength 2, possibly tight, that contains mostly 2-level factors. By a computer search of this starting array, we attempt to find as large a number of 2-level factors as possible that can be used in a new orthogonal array of strength 2 containing one additional factor at more than two levels. The method produces new orthogonal arrays for some parameters, and matches the best-known arrays for others. It is especially useful for finding arrays with one or two factors at more than two levels.     

An approach of constructing mixed-level orthogonal arrays of strength ⩾ 3

Orthogonal arrays (OAs), mixed level or fixed level (asymmetric or symmetric), are useful in the design of various experiments. They are also a fundamental tool in the construction of various combinatorial configurations. In this paper, we establish a general "expansive replacement method" for constructing mixedlevel OAs of an arbitrary strength. As a consequence, a positive answer to the question about orthogonal arrays posed by Hedayat, Sloane and Stufken is given. Some series of mixed level OAs of strength ≥3 are produced.