方差无穷非线性自回归序列的自加权L_1估计

对具有无穷方差的非线性自回归序列x_t=φ(x_(t-1),x_(t-2),…,x_(t-p),θ) ε_t,E(ε_t~2)=∞,利用局部二次近似和连续函数空间C(R~q)上弱收敛随机过程最小点的渐近性质,证明了若存在δ≥1,使得E|ε_t|~δ<∞成立,则θ满足一定条件的自加权L_1估计θ_(L_1)是渐近正态估计,Wald检验统计量也具有通常的x~2分布,为模型的统计推断提供了理论基础.     

基于小子样理论的坦克扭力轴疲劳寿命分析

基于小子样的思想,探讨扭力轴表面缺陷对其疲劳寿命的影响,有助于进一步找到降低扭力轴表面凹坑对扭力轴疲劳寿命影响的方法.     

Second-order linearity of the general signed-rank statistic

Let X₁,…, X_n be i.i.d. random variables symmetric about zero. Let R_i(t) be the rank of |X_i − tn^−1/2| among |X₁ − tn^−1/2|,…, |X_n − tn^−1/2| and T_n(t) = Σ_{i = 1}ⁿφ((n + 1)⁻¹R_i(t))sign(X_i − tn^−1/2). We show that there exists a sequence of random variables V_n such that sup_{0 ≤ t ≤ 1} |T_n(t) − T_n(0) − tV_n| → 0 in probability, as n → ∞. V_n is asymptotically normal.     

STATISTIC MODELING OF THE CREEP BEHAVIOR OF METAL MATRIX COMPOSITES BASED ON FINITE ELEMENT ANALYSIS

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Total Vertex Irregularity Strength of Dense Graphs

Consider a graph of minimum degree δ and order n. Its total vertex irregularity strength is the smallest integer k for which one can find a weighting such that for every pair of vertices of G. We prove that the total vertex irregularity strength of graphs with is bounded from above by . One of the cornerstones of the proof is a random ordering of the vertices generated by order statistics.     

微蜂窝环境中无线接收信号的特性分析

以微蜂窝系统为背景,研究了以测试射线法和虚拟源射线跟踪法为代表的射线跟踪技术,并用这两种方法对射线跟踪进行建模,分析比较两种模型的优缺点.利用这两种模型对实际情况进行计算,精确地找出所有给定情况的传播路径并可视化;对多波干涉的振幅进行了统计学分析,对宽带多波干涉现象进行了数学建模,并分析了合成波的包络统计特性.     

应用极值理论计算VaR的一种方法

在险价值(value at risk，简称VaR)是度量市场风险的一种普遍使用的工具，也是金融风险管理中的基础工具。计算VaR有许多不同的方法，本考虑了数据间的相关性，通过部分重叠数据分组的方法来计算VaR，发现所得到的VaR更符合实际；另外，我们对分组大小作了研究，发现分组数据大小的幂律存在，并对恒生指数作了具体分析。由此得出计算VaR的合理的数据分组大小。     

高等级公路路线方案的多目标量化优选

应用最小二乘法准则,并改进了层次分析法判断矩阵的构成,获得考虑专家评价水平的诸影响因素权向量;基于集值统计法和属性数学原理,建立了对高等级公路路线方案量化综合评价优选模型,并给出了计算实例.     

Edgeworth Expansions for Studentized Versions of Symmetric Finite Population Statistics

We show the validity of the one-term Edgeworth expansion for Studentized asymptotically linear statistics based on samples drawn without replacement from finite populations. Replacing the moments defining the expansion by their estimators, we obtain an empirical Edgeworth expansion. We show the validity of the empirical Edgeworth expansion in probability.