混水平列扩充设计的混偏差的下界

扩充设计作为一种新型的试验设计,近年来受到学者越来越广泛地关注.扩充设计包括初始设计与跟随设计两部分.在许多跟随设计中,在跟随阶段可以加入一些另外的2-水平或3-水平因子,因为它们在初始阶段可能被忽略但又十分重要.该文在均匀性准则下,给出了列扩充设计在混偏差下的解析表达式及相应的下界,列举了混偏差意义下的混水平列扩充近...     

扩大设计的中心化L_2-偏差的新下界

在构造两水平因子设计时,折叠反转是非常有用的技巧.折叠反转设计是通过改变初始设计中一个或多个因子的符号产生的跟随设计.通过把折叠反转设计中的处理加到初始设计中所得到的设计称为扩大设计.本文中,我们应用条件极值的方法得到了一般折叠反转方案下扩大设计在中心化L_2-偏差下一个新的下界,它可以作为寻找最优折叠反转方案的一个基准.     

三水平U-型设计在对称化L_(2-)偏差下的下界

均匀试验设计是部分因子设计的主要方法之,已被广泛地应用于工业生产、系统工程、制药及其他自然科学中.各种偏差被用来度量部分因子设计的均匀性.不管使用哪种偏差,关键的问题是寻找一个精确的偏差下界,因为它可以作为衡量设计均匀性的标准.本文应用条件极值的方法得到了三水平U-设计在对称化L_(2-)偏差下的下界,该下界可作为寻找均匀设计的一个基准.     

U-型设计的对称化L2-偏差的下界

均匀设计是部分因子设计的主要方法之一,已被广泛地应用于工业生产、系统工程、制药及其他自然科学中.各种偏差被用来度量部分因子设计的均匀性,其关键的问题是寻找一个精确的偏差下界,因为它可以作为衡量设计均匀性的标准.该文给出了4水平对称U-型设计的对称化L2-偏差的下界,以及2、3混水平和2、4混水平非对称U-型设计的对称化L2-偏差的下界.     

折叠反转设计的中心化L_2偏差值的一些下界

在二水平因子跟随试验中,折叠反转技术是一种非常典型的方法和技巧.这种技术通过折叠反转初始设计的一个或多个因子的水平符号,可以获得一个具有很好统计推断性质的新设计(称为折叠反转设计).初始设计与其折叠反转设计组合在一起所形成的新设计称为组合设计.该文在完全折叠反转方案和部分折叠反转方案下分别得到了组合设计在中心化L_2偏差下的一些下界.所得到的这些下界可以作为寻找最优折叠反转方案的一个标准,因此,该文的结果给出了用均匀性准则来寻找最优折叠反转方案的理论依据,进一步说明了均匀性在因子设计中的有用性.     

三水平部分因析设计的中心化L2偏差均值的几个结果

中心化L₂偏差已被用来作为部分因析设计均匀性的度量,并用来区分几何非同构设计.中心化L₂偏差均值也被用来度量部分因析设计均匀性,这样就可以对现有最小低阶混杂设计进行水平置换,从而获得中心化L₂偏差最小的均匀最小低阶混杂设计.本文里,我们针对三水平部分因析设计讨论中心化L₂偏差均值的性质,给出中心化L₂偏差均值与正交性准则,最小低阶矩混杂准则之间的解析关系,同时给出中心化L₂偏差均值的两个下界.     

混合偏差下多水平的均匀列扩展设计

列扩展设计是添加试验因素,并安排跟随试验的一个重要方法.本文分别构造了四水平和五水平的列扩展设计,基于平均混合偏差准则研究了该类设计的均匀性,并得到了四水平和五水平列扩展设计的平均混合偏差的一个新的下界.数值例子表明本文中构造的列扩展设计具有非常高的效率.     

Lee偏差下二三混水平因子设计的最优添加

根据序贯试验通过一个阶段试验接着另一个阶段试验不断扩充的特征,新的试验点将会被添加到已经选好的设计中,因此,如何设计一个好的序贯试验是一个非常有意义的问题.在Lee偏差度量下,本文研究均匀的非对称拓展设计,并用它来做序贯试验.本文建立二三混水平拓展设计的Lee偏差与其初始设计和附加设计的Lee偏差之间的解析关系,为构造(近似)均匀的拓展设计提供了理论支撑.为了给出筛选、评价拓展设计的准则,本文给出拓展设计Lee偏差的下界.此外,还通过一些数值例子来进一步说明、解释本文的理论结果.     

两水平U-型设计的扩大设计在投影加权对称偏差下的均匀性

均匀设计以其稳健和使用方便、灵活的特性而广受欢迎.为获得实验目标区域内散布均匀的设计点集,不同的均匀度量标准相继被提出.目前被广泛应用的有中心化L₂-偏差、可卷型L₂-偏差、混合偏差等.对称化L₂-偏差具有更好的几何性质,但受限于投影均匀性差的缺陷,使用范围十分有限.为了改进对称化L₂-偏差的低维投影均匀性,基于指数加权方式的投影加权对称化L₂-偏差的概念被提出,加权后的对称化L₂-偏差既能保留原偏差的各种优良性质,同时有效克服原来的缺陷并有更优异的表现.折叠翻转是构造因子设计时非常有用的技巧.本文利用投影加权对称偏差来作为评价折叠翻转方案的最优性准则,得到了两水平U-型设计在一般折叠翻转方案下扩大设计的投影加权对称偏差的下界,该下界可以作为寻找最优折叠翻转方案的基准.     

任意凸多面体上布点均匀性的度量

均匀性度量是构作均匀设计的基础,本文从距离概念出发,通过对称的方法,得到一种新的距离函数-势函数,并将势函数作为衡量任意凸多面体上布点均匀性好坏的准则.数值例子和多变量Kendall 协和系数检验表明,当试验区域限制在单位立方体上时,势函数与目前常用的两种偏差-中心化L_2-偏差和可卷L_2.偏差在度量布点均匀性方面结论一致.     

Optimum mechanism for breaking the confounding effects of mixed-level designs

Fractional factorial designs (FFD’s) are no doubt the most widely used designs in the experimental investigations due to their efficient use of experimental runs to study many factors simultaneously. One consequence of using FFD’s is the aliasing of factorial effects. Follow-up experiments may be needed to break the confounding. A simple strategy is to add a foldover of the initial design, the new fraction is called a foldover design. Combining a foldover design with the original design converts a design of resolution r into a combined design of resolution \(r+1\). In this paper, we take the centered \(L_2\)-discrepancy \(({\mathcal {CD}})\) as the optimality measure to construct the optimal combined design and take asymmetrical factorials with mixed two and three levels, which are most commonly used in practice, as the original designs. New and efficient analytical expressions based on the row distance of the \({\mathcal {CD}}\) for combined designs are obtained. Based on these new formulations, we present new and efficient lower bounds of the \({\mathcal {CD}}\). Using the new formulations and lower bounds as the benchmarks, we may implement a new algorithm for constructing optimal mixed-level combined designs. By this search heuristic, we may obtain mixed-level combined designs with low discrepancy.     

CONSTRUCTING UNIFORM DESIGNS WITH TWO- OR THREE-LEVEL

When the number of runs is large, to search for uniform designs in the sense of low-discrepancy is an NP hard problem. The number of runs of most of the available uniform designs is small (≤50). In this article, the authors employ a kind of the so-called Hamming distance method to construct uniform designs with two- or three-level such that some resulting uniform designs have a large number of runs. Several infinite classes for the existence of uniform designs with the same Hamming distances between any distinct rows are also obtained simultaneously. Two measures of uniformity, the centered L2-discrepancy (CD, for short) and wrap-around L2-discrepancy (WD, for short), are employed.     

Lower bounds and stochastic optimization algorithms for uniform designs with three or four levels

New lower bounds for three- and four-level designs under the centered -discrepancy are provided. We describe necessary conditions for the existence of a uniform design meeting these lower bounds. We consider several modifications of two stochastic optimization algorithms for the problem of finding uniform or close to uniform designs under the centered -discrepancy. Besides the threshold accepting algorithm, we introduce an algorithm named balance-pursuit heuristic. This algorithm uses some combinatorial properties of inner structures required for a uniform design. Using the best specifications of these algorithms we obtain many designs whose discrepancy is lower than those obtained in previous works, as well as many new low-discrepancy designs with fairly large scale. Moreover, some of these designs meet the lower bound, i.e., are uniform designs.

     

An efficient method for constructing uniform designs with large size

Many construction methods for (nearly) uniform designs have been proposed under the centered $L_2$ -discrepancy, and most of them are only suitable for constructing designs with small size. This paper proposes a new method, called mixture method (MM), to construct nearly symmetrical/asymmetrical uniform designs with large number of runs and/or large number of factors. The new method has the “better than given” property, i.e., the resulting design is better than existing designs in the sense of the pre-decided criterion. Moreover, the computational speed of MM is faster than most existing methods.     

Centered -discrepancy of random sampling and Latin hypercube design, and construction of uniform designs

In this paper properties and construction of designs under a centered version of the -discrepancy are analyzed. The theoretic expectation and variance of this discrepancy are derived for random designs and Latin hypercube designs. The expectation and variance of Latin hypercube designs are significantly lower than that of random designs. While in dimension one the unique uniform design is also a set of equidistant points, low-discrepancy designs in higher dimension have to be generated by explicit optimization. Optimization is performed using the threshold accepting heuristic which produces low discrepancy designs compared to theoretic expectation and variance.

     

On the Isomorphism of Fractional Factorial Designs

Two fractional factorial designs are called isomorphic if one can be obtained from the other by relabeling the factors, reordering the runs, and switching the levels of factors. To identify the isomorphism of two s-factor n-run designs is known to be an NP hard problem, when n and s increase. There is no tractable algorithm for the identification of isomorphic designs. In this paper, we propose a new algorithm based on the centered L₂-discrepancy, a measure of uniformity, for detecting the isomorphism of fractional factorial designs. It is shown that the new algorithm is highly reliable and can significantly reduce the complexity of the computation. Theoretical justification for such an algorithm is also provided. The efficiency of the new algorithm is demonstrated by using several examples that have previously been discussed by many others.     

A note on optimal foldover four-level factorials

The foldover is a quick and useful technique in construction of fractional factorial designs, which typically releases aliased factors or interactions. The issue of employing the uniformity criterion measured by the centered L ₂-discrepancy to assess the optimal foldover plans was studied for four-level design. A new analytical expression and a new lower bound of the centered L ₂-discrepancy for fourlevel combined design under a general foldover plan are respectively obtained. A necessary condition for the existence of an optimal foldover plan meeting this lower bound was described. An algorithm for searching the optimal four-level foldover plans is also developed. Illustrative examples are provided, where numerical studies lend further support to our theoretical results. These results may help to provide some powerful and efficient algorithms for searching the optimal four-level foldover plans.     

A note on lower bound of centered L2-discrepancy on combined designs

This note provides a theoretical justification of optimal foldover plans in terms of uniformity. A new lower bound of the centered L ₂-discrepancy values of combined designs is obtained, which can be used as a benchmark for searching optimal foldover plans. Our numerical results show that this lower bound is sharper than existing results when more factors reverse the signs in the initial design.     

可卷型L_2-偏差的水平置换方法构造混合水平设计

提出了一种通过置换因子的水平来构造具有较小可卷型L_2-偏差的混合水平均匀设计的新方法.首先建立了混合水平设计的平均可卷型L_2-偏差与广义字长型的定量关系,并以具有较小广义字长型的混合水平设计为初始设计,对其作水平置换,计算其可卷型L_2-偏差,找到具有最小的可卷型L_2-偏差的设计就是相对较好的设计.为了使算法更加有效,还运用了可卷型L_2-偏差的两个性质.数值结果显示通过这种方法构造的设计在可卷型L_2-偏差下表现良好.     

Uniformity in double designs

In constructing two-level fractional factorial designs, the so-called doubling method has been employed. In this paper, we study the problem of uniformity in double designs. The centered L2-discrepancy is employed as a measure of uniformity. We derive results connecting the centered L2-discrepancy value of D（X） and generalized wordlength pattern of X, which show the uniformity relationship between D（X） and X. In addition, we also obtain lower bounds of centered L2-discrepancy value of D（X）, which can be used to assess uniformity of D（X）.