The problem of confounding in two-factor experiments

The problem of confounding in an axb experiment is studied without indexing the set of levels of each factor a priori by an algebraic object. General arithmetic restrictions on a and b are derived, leading to a'geometric reformulation of the problem when a or b is prime. This reformulation reduces to the problem of the existence of a generalized Hadamard matrix of specified order.     

Analysis of variance of balanced fractional 2n factorial designs of resolution 2l+1

By use of the algebraic structure of the triangular multidimensional partially balanced association scheme, we present the analysis of variance and the hypotheses testing of a balanced fractional 2ⁿfactorial design of resolution 2l+1, which is derived from a balanced array of strength 2l.     

一类带谱参数的奇异Sturm-Liouville算子Ⅰ

研究了一类具有转换条件且边界条件中带谱参数的奇异Storm-Liouville问题．将对上述问题特征值的研究，转化为考虑定义在Hilbert空间H中一个算子A的特征值问题．     

Scheffés mixed model for multivariate repeated measures:a relative efficiency evaluation

Scheffé’s mixed model, generalized for application to multivariate repeated measures, is known as the multivariate mixed model (MMM). The primary advantages the MMM are (1) the minimum sample size required to conduct an analysis is smaller than for competing procedures and (2) for certain covariance structures, the MMM analysis is more powerful than its competitors. The primary disadvantage is that the MMM makes a very restrictive covariance assumption; namely multivariate sphericity. This paper shows, first, that even minor departures from multivariate sphericity inflate the size of MMM based tests. Accordingly, MMM analyses, as computed in release 4.0 of SPSS MANOVA (SPSS Inc., 1990), can not be recommended unless it is known that multivariate sphericity is satisfied. Second, it is shown that a new Box-type (Box, 1954) Δ-corrected MMM test adequately controls test size unless departure from multivariate sphericity is severe or the covariance matrix departs substantially from a multiplicative-Kronecker structure. Third, power functions of adjusted MMM tests for selected covariance and noncentrality structures are compared to those of doubly multivariate methods that do not require multivariate sphericity. Based on relative efficiency evaluations, the adjusted MMM analyses described in this paper can be recommended only when sample sizes are very small or there is reason to believe that multivariate sphericity is nearly satisfied. Neither the e-adjusted analysis suggested in the SPSS MANOVA output (release 4.0) nor the adjusted analysis suggested by Boik (1988) can be recommended at all.     

The problem of confounding in two-factor experiments

The problem of confounding in an axb experinent is studied without indexing the set of levels of each factor a prior by an algebraic object. General arithmetic restrictions on a and b are derived, leading to a geometric reformulation of the problem when a or b is prime. This reformulation reduces to the problem of the existence of a generalized Hadamard matrix of specified order.     

Mixed-Mode Oscillations Due to a Singular Hopf Bifurcation in a Forest Pest Model

In a forest pest model, young trees are distinguished from old trees. The pest feeds on old trees. The pest grows on a fast scale, the young trees on an intermediate scale, and the old trees on a slow scale. A combination of a singular Hopf bifurcation and a “weak return” mechanism, characterized by a small change in one of the variables, determines the features of the mixed-mode oscillations. Period-doubling and saddle-node bifurcations lead to closed families (called isolas) of periodic solutions in a bifurcation corresponding to a singular Hopf bifurcation.     

二次松弛法求解资源配置型整数规划

针对资源配置型整数规划问题,将与优化机制无关的两类约束条件作二次松弛处理,应用领域整点搜索法求解资源配置型整数规划,大大地降低了运算规模和求解难度,为在管理实践中应用整数规划方法求解规模较大的复杂问题创造了有利条件.     

Simple and multiple correspondence analysis for ordinal-scale variables using orthogonal polynomials

Correspondence analysis (CA) has gained a reputation for being a very useful statistical technique for determining the nature of association between two or more categorical variables. For simple and multiple CA, the singular value decomposition (SVD) is the primary tool used and allows the user to construct a low-dimensional space to visualize this association. As an alternative to SVD, one may consider the bivariate moment decomposition (BMD), a method of decomposition that involves using orthogonal polynomials to reflect the structure of ordered categorical responses. When the features of BMD are combined with SVD, a hybrid decomposition (HD) is formed. The aim of this paper is to show the applicability of HD when performing simple and multiple CA.     

A control chart based on sample ranges for monitoring the covariance matrix of the multivariate processes

For the univariate case, the R chart and the S ² chart are the most common charts used for monitoring the process dispersion. With the usual sample size of 4 and 5, the R chart is slightly inferior to the S ² chart in terms of efficiency in detecting process shifts. In this article, we show that for the multivariate case, the chart based on the standardized sample ranges, we call the RMAX chart, is substantially inferior in terms of efficiency in detecting shifts in the covariance matrix than the VMAX chart, which is based on the standardized sample variances. The user's familiarity with sample ranges is a point in favor of the RMAX chart. An example is presented to illustrate the application of the proposed chart.     

Mean Squared Error Matrix Comparisons of Some Restricted Almost Unbiased Estimators

In this article, we introduce two almost unbiased estimators for the vector of unknown parameters in a linear regression model when additional linear restrictions on the parameter vector are assumed to hold. Superiority of the two estimators under the mean squared error matrix (MSEM) is discussed. Furthermore, a numerical example and simulation study are given to illustrate some of the theoretical results.