含过程变量的K模型最优正交区组设计

混料试验设计是研究混合物成份比例的试验设计,最优设计则是混料试验设计中重要的研究方向,其中以D-最优设计应用最为广泛.文中对K模型混料试验与最优准则进行介绍,对其含过程变量的3分量二阶K模型给出满足D-最优准则的正交区组设计,最后推广至q分量情形并证明.     

可加混料模型参数估计的D-最优正交区组设计

对于含有过程变量的二阶可加混料模型,利用正交拉丁方,研究了其参数估计的D-最优正交区组设计,一般性地给出了q分量时的D-最优正交区组设计的谱点结构,并以此推广得到含有相同或不同下界约束时的最优正交设计的谱点。     

基于二阶指数混料模型的A-最优设计

使用最优设计理论研究混料试验的过程中,需考虑混料模型对应的函数向量。当函数向量为非线性函数时,虽可使用Taylor级数进行近似,但级数阶的选取必然使得误差的存在,给试验带来偏差。本文旨在使用最优设计理论,研究q分量二阶指数混料模型的A-最优设计问题,并得到了该模型下的最优设计。且从设计效率的角度,研究了不同分量下的A-最优设计效率,为确定设计优劣提供了一个依据,并给出了进一步可以研究的问题。     

多响应Scheffé混料模型最优设计

在工业生产及科学实验中经常需要对多个混料试验指标进行统计分析,由于各响应指标之间存在一定的相关性,关于这类模型下最优设计的构造就变得比较复杂.本文在特定的相关条件下,构造了p维响应二阶Scheffe模型的D-最优设计,并讨论了二响应Scheffe模型的Ds-最优设计与A-最优设计.这三种最优设计的柱点测度只与变量个数有关而与响应协方差无关.     

混合效应模型中的D-最优设计

本研究的是混合效应-Ⅱ模型，首先，给出二阶模型存在D-最优回归设计点的必要条件。其次，组合D-最优回归设计和D-最优区组设计BIBD构造D-最优设计并给出寻找新的D-最优设计点的方法。以两种有代表性的组合误差分布为例，阐明新的D-最优设计点的获得过程，并给出了数值结果。     

混料试验的拟分量变换设计

在混料试验中有两类重要的设计,分别是最优设计与均匀设计.为了构造一类能兼顾最优性与均匀性的混料设计,本文在Scheffe型设计的基础上提出了混料拟分量变换设计.首先定义了填充格子点集并讨论了它的相关性质,然后以填充格子点集作为NT-net用于度量点集的均匀程度,证明了对称区域内的中心压缩拟分量变换设计最均匀,以及D-,A-最优设计的拟分量变换设计具有最优性不变的性质.通过实例分析,验证了拟分过变换设计的有效性,并且这种设计能保证在变换区域内的最优性以及整体区域的均匀性.这种方法所建立的模型与真实模型更接近,更稳健.     

不确定约束混料域上的D-最优试验设计

使用混料试验设计研究中草药配方的过程中,需要考虑一类特殊的约束-加法取小不等式组成的不确定约束系统。该系统虽可简洁表达中药配方复杂的分量间关系,但却给试验设计过程带来了困难。本文旨在构造该约束系统形成约束域上的D-最优设计,首先给出一个将该系统化为线性约束不等式组的方法,然后使用改进的CONSIM算法求出极端顶点点集,并使用改进的MDRS算法求得D-最优的设计点集和该点集设计达D-最优时的测度,最后给出一个例子来展示算法,并提出有待进一步研究的问题。     

对数项混料模型及其D-最优性

本文提出了具有对数项的新混料模型,它是Soheffe典型多项式模型的推广,并且是与Draper和st.John的倒数项混料模型相平行的另一种形式。对于在三分量和四分量的情形,我们利用Kiefor-Wolfowitz的等价定理,Fedorov和Wynn的方法以及本文提出的组合搜索所组成的计算机辅助设计方法,分别证明和构造了一次和二次的对数项混料模型的近似D-最优测度设计和D_n-最优确切设计。此外,本文还给出新模型在工程陶瓷材料研究中的实际应用。同时,把它与国外文献上的实例作了统计分析的比较。     

混料模型D—最优试验设计的改进DE算法研究

差分进化算法(Differential Evolution Algorithm)有较强的全局收敛能力和稳健性,应用该算法可解决工程学、计算机科学等领域的一些复杂优化问题。本文介绍一个改进DE算法,该算法适用于混料模型的近似最优设计问题,对于具有附加约束试验域的混料问题也可高效求解。最后,本文给出应用改进DE算法求解混料模型D-最优试验设计的例子。     

含定性因子的二阶Scheff′e模型多目标最优设计北大核心

在实际的很多情形中,混料试验都具有多个目标,且响应变量不仅受到各分量的影响,还会受到其他定性因子变量的影响.文中基于一类含定性因子的混料模型,通过求解效率和极值得到多目标最优设计,并证明了该设计满足相应组合最优性.实例证明,该方法同样适用于3个或更多目标优化设计问题.     

Robustness of Bayesian D-optimal design for the logistic mixed model against misspecification of autocorrelation

In medicine and health sciences mixed effects models are often used to study time-structured data. Optimal designs for such studies have been shown useful to improve the precision of the estimators of the parameters. However, optimal designs for such studies are often derived under the assumption of a zero autocorrelation between the errors, especially for binary data. Ignoring or misspecifying the autocorrelation in the design stage can result in loss of efficiency. This paper addresses robustness of Bayesian D-optimal designs for the logistic mixed effects model for longitudinal data with a linear or quadratic time effect against incorrect specification of the autocorrelation. To find the Bayesian D-optimal allocations of time points for different values of the autocorrelation, under different priors for the fixed effects and different covariance structures of the random effects, a scalar function of the approximate variance–covariance matrix of the fixed effects is optimized. Two approximations are compared; one based on a first order penalized quasi likelihood (PQL1) and one based on an extended version of the generalized estimating equations (GEE). The results show that Bayesian D-optimal allocations of time points are robust against misspecification of the autocorrelation and are approximately equally spaced. Moreover, PQL1 and extended GEE give essentially the same Bayesian D-optimal allocation of time points for a given subject-to-measurement cost ratio. Furthermore, Bayesian optimal designs are hardly affected either by the choice of a covariance structure or by the choice of a prior distribution.     

混料试验的D—最优对称设计,算法及应用

本文着重研究了混料试验的D—最优对称设计.基于Fedorov及Atwood的迭代方法,作者给出一个构造D—最优对称设计的改进算法.这个新算法由双循环迭代构成:从初始设计中减去最小方差对称点的设计测度;增加设计测度于最大方差的对称设计点,同时,本算法还只在对称子区域中寻找最大方差设计点,这样就使得Fedorov算法的收敛速度有了显著地提高,并能构造出更高效的D—最优对称设计.另外还给出一些构造实例.     

A New Design Criterion When Heteroscedasticity is Ignored

This paper examines the construction of optimal designs when one assumes a homoscedastic linear model, but the underlying model is heteroscedastic. A criterion that takes this type of misspecification into account is formulated and an equivalence theorem is given. We also provide explicit optimal designs for single-factor and multi-factor experiments under various heteroscedastic assumptions and discuss the relationship between the D-optimal design sought here and the conventional D-optimal design.     

A-optimal designs for an additive cubic model

Chan et al. (1998a) obtained A-optimal designs for an additive quadratic mixture model for q≥3 mixture components. In this paper, we obtain the A-optimal designs for an additive cubic model for q≥3 mixture components using the class of symmetric weighted centroid designs based on barycentres of various depths. We observe that barycentres of depths 0 and 2 are possible support points for an A-optimal design. We have also given the optimal weights of A-optimal designs for 3≤q≤17.     

基于特殊混料模型D-最优设计搜索的交换点式门限接受算法

在搜索混料模型D-最优设计的计算机算法领域,主流算法包括经典的Fedorov算法,以及元启发类算法,但两者在一些特定的优化问题上,分别在收敛速度和收敛精度方面有进一步提升的空间.文章分别探讨了可能造成这种情况的两类算法各自的局限性,并采取优势互补的策略,构建了交换点式门限接受算法,即ETA (exchange threshold accepting)算法.以含倒数项混料模型为例,文章验证了ETA算法生成设计的D-最优性,并分别与Fedorov算法和元启发类的ProjPSO算法作比较.结果表明,至少在某些特殊的混料模型D-最优设计的搜索方面,ETA算法在收敛速度和精度方面均具有一定的优势.     

Minimax designs for linear regression models with bias in a reproducing kernel Hilbert space in a discrete set

Consider the design problem for estimation and extrapolation in approximately linear regression models with possible misspecification. The design space is a discrete set consisting of finitely many points, and the model bias comes from a reproducing kernel Hilbert space. Two different design criteria are proposed by applying the minimax approach for estimating the parameters of the regression response and extrapolating the regression response to points outside of the design space. A simulated annealing algorithm is applied to construct the minimax designs.These minimax designs are compared with the classical D-optimal designs and all-bias extrapolation designs. Numerical results indicate that the simulated annealing algorithm is feasible and the minimax designs are robust against bias caused by model misspecification.     

广义不确定周期时变系统的鲁棒非脆弱控制

研究了状态矩阵具有不确定性的广义周期时变系统的鲁棒非脆弱控制问题.利用线性矩阵不等式(LMI)方法,分别对控制器增益具有加法式摄动和乘法式摄动两种情形加以讨论,而非脆弱控制器的设计可以通过求解一组线性矩阵不等式得到.最后,数值例子说明了所给方法的有效性.     

Bipartite Graphs Kn,n+r- A (|A| ≤ 3) Determined by Their Cycle Length Distributions

The cycle length distribution of a graph G of order n is a sequence (c1 (G),..., cn (G)), where ci(G) is the number of cycles of length i in G. In general, the graphs with cycle length distribution (c1(G),...,cn(G)) are not unique. A graph G is determined by its cycle length distribution if the graph with cycle length distribution (c1 (G),..., cn (G)) is unique. Let Kn,n+r be a complete bipartite graph and A(∈)E(Kn,n+r). In this paper, we obtain: Let s > 1 be an integer. (1) If r = 2s, n > s(s - 1) + 2|A|, then Kn,n+r - A (A(∈)E(Kn,n+r),|A| ≤ 3) is determined by its cycle length distribution; (2) If r = 2s + 1,n > s2 + 2|A|, Kn,n+r - A (A(∈)E(Kn,n+r), |A| ≤ 3) is determined by its cycle length distribution.     

定数截尾试验下两参数Weibull分布尺度参数的最优稳健Bayes估计

以Г-后验期望损失作为标准,研究了定数截尾试验下两参数W e ibu ll分布尺度参数θ的最优稳健Bayes估计问题.假设尺度参数θ的先验分布在分布族Г上变化,形状参数β已知时,在0-1损失下,得到了θ的最优稳健区间估计,在均方损失下得到θ的最优稳健点估计及区间估计;β未知时,得到了θ的最优稳健点估计及区间估计.最后给出了数值例子,说明了方法的有效性.