正交平衡区组设计统计分析模型的参数估计

正交平衡区组设计(或者广义正交表)的数据分析类似于正交拉丁方(或者正交表)的数据分析,但试验次数大幅减少.引入了相遇平衡区组设计矩阵象的概念,定义了一种基于正交相遇平衡区组设计(或者广义正交表)的统计分析模型,根据这个模型,推导得到了参数的最小二乘估计.     

正交平衡区组设计统计分析模型参数估计的矩阵表达

正交平衡区组设计(或者广义正交表)是一种类似于正交拉丁方(或者正交表)的新设计,但试验次数大幅减少.定义了一种基于正交相遇平衡区组设计(或者广义正交表)的统计分析模型,根据这个模型,给出了参数的最小二乘估计的矩阵形式.     

广义正交表方差分析的矩阵计算形式

广义正交表是一种类似于正交表的新设计.它是正交表的推广,可以像正交表一样进行试验设计和数据分析,但试验次数大幅减少.方差分析是统计推断的内容之一,本文从自由模型出发考虑方差分析,采用矩阵象技术,给出了广义正交表方差分析的矩阵计算形式,借助SAS软件可以方便快速的实现.     

正交平衡区组设计矩阵象的概念及其基本定理

给出了正交平衡区组设计(或广义正交表)的矩阵象的概念及例子,证明了矩阵象的几个基本定理,得出了正交平衡区组设计的正交性等价于矩阵象的正交性的重要结论,从而为利用正交平衡区组设计进行数据分析提供了理论依据.     

正交平衡区组设计统计分析模型参数估计的分布特征研究

正交平衡区组设计(或者广义正交表)是一种类似于正交拉丁方(或者正交表)的新设计,但试验次数大幅减少.通过对正交平衡区组设计统计分析模型参数估计的分布特征进行了深入研究.研究发现,在试验数据正态性的情况下,各种参数估计也服从正态分布,并且各种参数的最小二乘估计都是无偏的,得到了各种参数估计的方差和独立性性质.     

广义正交表交互作用自由度分布的判定

正交相遇平衡区组设计(或者广义正交表)是一种类似于正交拉丁方(或者正交表)的新设计,但试验次数大幅减少.与交互作用有关的混杂现象是正交设计也是正交相遇平衡区组设计的难点,利用矩阵象技术,给出了广义正交表交互作用自由度分布的判定方法,借助于SAS软件可以方便快速的进行判定.     

平衡区组正交表与正交表的比较及应用

对平衡区组正交表和正交表进行的数据分析作了比较,表现出平衡区组正交表的优良性.给出了平衡区组正交表的应用实例,且对试验结果进行了分析.同时得出,分别用平衡区组正交表GL6(3221)和正交表L9(34)处理同一个问题时,所得试验结果一致.     

平衡区组正交表的Kronecker积替换构造

平衡区组正交表是一种有别于传统正交表的新设计,这种设计保持了正交表对应区组设计的相遇平衡性、组间平衡性和正交平衡性,因此也具备了传统平衡不完全区组(BIB)设计、拉丁方设计、拉丁矩阵设计等数据分析的优良性质和相应的组合分析性质.性质保证了平衡区组正交表与正交表一样具有试验数据分析结论的再现性和试验中心的稳定性,是比广义正交表更加接近于正交表的一种试验设计表.通过对平衡区组正交表的构造方法进行研究,发现在已知平衡区组正交表的基础上,利用矩阵象理论,经过Kronecker积替换构造,可以构造许多新的平衡区组正交表.     

链式区组设计与广义正交表

在区组设计理论中,当区组水平(处理)数很大时,经常采用一种链式区组设计.在基于链式区组设计收集数据时,相应的设计表已经不是平衡不完全区组设计(BIBD),因此基于BIBD的数据分析发展成的链式区组设计的数据分析方法将存在不足之处,突出的特点是试验的数据分析结论不再具有再现性.为了保证新的设计表仍然具有试验数据分析结论的再现性,至少需要相应的设计表是广义正交表,即至少需要保持新的设计表具有相遇平衡和正交平衡性质.也就是说:在某种条件下,基于链式区组设计收集数据,也能保证试验数据分析的结论具有再现性,仅需相应的新设计表是广义正交表即可.研究发现:在链式区组设计中,相应的设计表在某些条件下可以是广义正交表.从广义正交表的角度来看,证明了,将对称BIBD作为小组下标,由此构造的链式区组设计对应的设计表,仍然是广义正交表,从而说明了链式区组设计方法可以在试验设计理论中有条件的使用.这也启发可以把链式区组试验设计方法扩充成广义正交表的构造方法.     

区组设计的矩阵象的计算及其应用

平衡区组设计是对传统平衡不完全区组设计(BIBD)、部分平衡不完全区组设计(PBIBD)和拉丁矩阵(或拉丁方)设计等区组设计的一种推广,这种区组设计比传统区组设计的多种平衡条件更弱,满足新平衡条件的平衡区组设计更多,也更容易构造,并且新构造出的区组设计仍然保持着原有各种形式的区组设计的各种平衡性,因而保持着在统计分析中的优良性质,从而可以和原来各种形式平衡区组设计一样用于试验设计和统计分析.研究新的一般区组设计的性质的一个重要工具是它们的矩阵象性质.首先对一般区组设计的矩阵象的定义和计算进行研究,这些矩阵象的运算性质和正交表的矩阵象运算性质基本类似,可以和正交表的矩阵象一样进行应用.正交表矩阵象的主要应用有两方面:正交表构造和数据分析研究.先把平衡区组设计的矩阵象应用于简单构造平衡区组设计.     

广义正交表正交性的等价条件

广义正交表是一种类似于正交表的新设计.正交平衡性是广义正交表必须满足的基本要求之一,它是正交表正交性的推广,它能够使得试验因子在方差分析中保持柯赫伦定理成立,因而可以像正交表一样进行试验设计和方差分析,从而不但保证其数据分析模型符合"不自生"逻辑,而且也可以保证试验因子的各种关系比较的数据分析结论具有客观一致性和可重复再现性,但试验次数大幅减少.利用矩阵象技术,提出并证明了广义正交表的组合正交性不但等价于其矩阵象的正交性,而且也等价于其广义关联矩阵的正交性.借助于SAS软件可以方便快速的验证某些区组设计相应的行列设计是否为广义正交表.     

Alias balanced and alias partially balanced fractional 2 m factorial designs of resolution 2l+1

As a generalization of alias balanced designs due to Hedayat, Raktoe and Federer [5], we introduce the concept of alias partially balanced designs for fractional 2 ^m factorial designs of resolution 2l+1. All orthogonal arrays of strength 2l yield alias balanced designs. Some balanced arrays of strength 2l yield alias balanced and alias partially balanced designs. In particular, simple arrays which are a special case of balanced arrays yield alias partially balanced designs. At most 2 ^m −1 alias balanced (or alias partially balanced) designs are generated from an alias balanced (or alias partially balanced) design by level permutations. This implies that alias balanced or alias partially balanced designs need not be orthogonal arrays or balanced arrays of strength 2l.     

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.     

Balanced arrays of strength two from block designs

Some constructions of balanced arrays of strength two are provided by use of rectangular designs, group divisible designs, and nested balanced incomplete block designs. Some series of such arrays are also presented as well as orthogonal arrays, with illustrations. © 2002 Wiley Periodicals, Inc. J Combin Designs 10: 303–312, 2002; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/jcd.10016     

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.     

Optimal balanced 27 fractional factorial designs of resolutionV,withN≦42

Summary In this paper, we present a class of fractional factorial designs of the 2⁷ series, which are of resolutionV. Such designs allow the estimation of the general mean, the main effects and the two factors interactions (29 parameters in all for the 2⁷ factorial) assuming that the higher order effects are negligible. For every value ofN (the number of runs) such that 29≦N≦42, we give a resolutionV design that is optimal (with respect to the trace criterion) within the subclass of balanced designs. Also, for convenience of analysis, we present for each design, the covariance matrix of the estimates of the various parameters. As a by product, we establish many interesting combinatorial theorems concerning balanced arrays of strength four (which are generalizations of orthogonal arrays of strength four, and also of balanced incomplete block designs with block sizes not necessarily equal).