基本变量区域重要性测度及其稀疏网格解

为了提高现有基本变量对样本均值贡献的区域重要性测度指标的稳定性和收敛性, 提出了一个新的衡量基本变量内部各个区域对输出均值影响的重要性测度指标.并将其进一步扩展提出了一个衡量基本变量内部各个区域对输出总方差分解式中一阶方差影响的区域重要性测度指标.分析了所提指标的性质, 并探讨了它们与现有基本变量对样本均值贡献区域重要性测度指标和对样本方差贡献的区域重要性测度 指标之间的关系. 另外, 针对所提指标的特点, 还建立了其求解高效的稀疏网格积分法.算例结果表明, 所提新的基本变量对输出均值贡献的区域重要性测度指标不仅继承了现有指标的优点, 而且比现有指标具有更高的收敛性和稳定性.所提基本变量对一阶方差贡献的区域重要性指标能够在基本变量对样本方差贡献区域重要性测度的基础上, 进一步提供基本变量内部各个区域对总方差的一阶分量的影响信息.而所建稀疏网格积分法可以在保证计算精度的同时大幅度提高基本变量区域重要性分析的效率.     

Two kinds of contact problems in three-dimensional icosahedral quasicrystals

Two kinds of contact problems, i.e., the frictional contact problem and the adhesive contact problem, in three-dimensional(3D) icosahedral quasicrystals are discussed by a complex variable function method. For the frictional contact problem, the contact stress exhibits power singularities at the edge of the contact zone. For the adhesive contact problem, the contact stress exhibits oscillatory singularities at the edge of the contact zone. The numerical examples show that for the two kinds of contact problems, the contact stress exhibits singularities, and reaches the maximum value at the edge of the contact zone. The phonon-phason coupling constant has a significant effect on the contact stress intensity, while has little impact on the contact stress distribution regulation. The results are consistent with those of the classical elastic materials when the phonon-phason coupling constant is 0. For the adhesive contact problem, the indentation force has positive correlation with the contact displacement, but the phonon-phason coupling constant impact is barely perceptible. The validity of the conclusions is verified.     

径向非均匀介质中圆形夹杂的动应力分析

基于复变函数理论,研究了径向非均匀弹性介质中均匀圆夹杂对弹性波的散射问题. 介质的非均匀性体现在介质密度沿着径向按幂函数形式变化且剪切模量是常数. 利用坐标变换法将变系数的非均匀波动方程转为标准亥姆霍兹(Helmholtz) 方程. 在复坐标系下求得非均匀基体和均匀夹杂同时存在的位移和应力表达式. 通过具体算例分析了圆夹杂周边的动应力集中系数(DSCF). 结果表明:基体与夹杂的波数比和剪切模量比,基体的参考波数和非均匀参数对动应力集中有较大的影响.      

关于任意随机序列的一个强大数定理

This note is devoted to introduce a new concept of conditionally dominated random variables.Under suitable restrict conditions,a general strong law of large numbers for arbitrary continuous random variables is obtained.     

Construction of additional variables conforming to a common factor model

It is shown that if a random p-vector x conforms to an r-common factor model and an external variable Z is given, then there exists a family of linear combinations X of x and Z such that [x′: X]′ conforms to an r-common factor model     

目标规划中权系数的几何意义

本用平面仿射坐标的观点,解释了目标规划中权系数的几何意义,借助于坐标变换,可改进图解法的效果。     

A Test of Multivariate Independence Based on a Single Factor Model

A test of the independence of two sets of variables is developed to have high power against a special family of dependence. In this each set of variables has the structure of a single factor model and the dependence is solely via the correlation γ between the underlying latent variables. This is a model with only one nonzero canonical correlation. It is shown that a test based on the maximum likelihood estimate of γ is appreciably more powerful than that based on r₁, the largest sample canonical correlation. If, however, the model is used, not just as a family of alternatives but as the basis for interpretation, and if substantial cross-correlation is present then the procedure is essentially equivalent to the use of r₁.     

On the Convergence Behaviour of Variable Stepsize Multistep Methods for Singularly Perturbed Problems

In this note, we investigate the convergence behaviour of linear multistep discretizations for singularly perturbed systems, emphasising the features of variable stepsizes. We derive a convergence result for A()-stable linear multistep methods and specify a refined error estimate for backward differentiation formulas. Important ingredients in our convergence analysis are stability bounds for non-autonomous linear problems that are obtained by perturbation techniques.     

连续随机变量的随机独立性与回归独立性

回归独立性是指给定随机变量 X时 ,随机变量 Y的条件期望 E( Y|X)不依赖于 X.前人讨论了离散型随机变量回归独立性与随机独立性的关系 ,得到了二者等价的充分必要条件 .对连续型随机变量的情形加以讨论 ,获到了二者等价的几个充分必要条件 ,并说明在统计分析中的应用 .     

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The heteroscedasticity is inevitable for the panel data modeling in economics. The two-stage estimation method is a better means to study the heteroscedasticity, in which the basis is to select only one independent variable for samples grouping, it can cause the information used is incomplete. In this paper, we propose to select several variables for grouping using variable selection method, then k-mean algorithm is used to cluster, so the samples classification can be achieved and the heteroscedasticity estimation can be obtained. The results of real example analysis show that the method presented in this paper has obvious advantages in effectiveness and feasibility.