普阿松分布的极限分布

本主要阐明概率论中三个重要分布之间的关系，重点证明了普阿松分布的极限分布是正态分布。     

基于截尾正态分布的最优过程均值的确定

过程均值的选择对生产率的提高以及产品质量改进非常重要,因为它直接影响到过程的缺陷率、材料费用、重加工费及产品性能偏离目标值对顾客造成的损失等.本文讨论了在不对称田口质量损失函数下截尾正态分布的最优过程均值的确定问题.通过灵敏度分析,研究了过程参数对过程均值选择的影响.     

基于正态分布求解两类反常积分

对两类反常积分,进行巧妙处理,构造成正态分布函数形式,利用正态分布函数的正则性进行求解.     

多元正态分布的一个性质及其应用

基于多元正态分布和一元正态分布之间的一个重要性质,将其应用于问题求解,实例说明,巧妙借助该性质可使相关问题的求解过程大为简化.     

正、余弦函数无穷乘积展开式的两个应用

通过对正、余弦函数无穷乘积展开式先取对数,再求导或再积分的方法,可获得两类无穷级数的和以及两类积分的无穷级数表示.     

问题驱动和思维引导下的《正态分布》教学设计

为了充分调动学生学习概率的积极性并培养学生分析、解决问题的能力,在《正态分布》教学设计中,运用问题驱动和思维引导的教学理念,从高尔顿钉板实验出发,借助二项分布的极限分布——正态分布建立模型,学习正态分布的概率特性,引入中心极限定理,拓展正态分布的性质及应用.     

多维正态分布函数的表示

在许多金融问题的研究中,如金融衍生产品定价、金融风险度量与管理等领域,经常用到多维正态分布函数.在概率论与数理统计文献中只给出了一维标准正态分布表,而多维正态分布函数的计算问题没有给出具体的算法.本文给出了多维正态分布函数与一维标准正态分布函数的关系式,从而解决了多维正态分布函数的计算问题.     

无穷级数的∞↑∑n=1 1/（2n-1）^2k一种计算方法

根据无穷多项式理论,将余弦函数的幂级数展开式构造成无穷乘积的形式．并且利用ln（1＋x）幂级数展开,得到∞↑∑n=1 1/（2n-1）^2k（k为正整数）的一种计算方法．     

无穷级数∞∑(n＝1)1/(2n-1)2k的一种计算方法

根据无穷多项式理论,将余弦函数的幂级数展开式构造成无穷乘积的形式.并且利用ln(1+x)幂级数展开,得到∞∑(n-1)1/(2n-1)2k(R为正整数)的一种计算方法.     

应用正态分布的线性不变性研究二维正态分布性质

本文应用正态分布的线性不变性来研究二维正态分布的性质,包括可加性、边际分布、特征函数、条件分布、条件数学期望.     

关于二项分布的极限分布的一个注记

讨论了用泊松分布和正态分布近似表示二项分布的精确程度问题,对于泊松分布,指出了它对二项分布B(n,p)的概率值的近似精确与否基本上只依赖于参数p而不依赖于n,并说明了经验条件“np≤5”的不确切.     

Conditions for the convergence in distribution of maxima of stationary normal processes

The asymptotic distribution of the maximum M_n=max_1?t?nξ_t in a stationary normal sequence ξ₁,ξ,… depends on the correlation r_t between ξ₀ and ξ_t. It is well known that if r_t log t → 0 as t → ∞ or if Σr²_t<∞, then the limiting distribution is the same as for a sequence of independent normal variables. Here it is shown that this also follows from a weaker condition, which only puts a restriction on the number of t-values for which rt log t islarge. The condition gives some insight into what is essential for this asymptotic behaviour of maxima. Similar results are obtained for a stationary normal process in continuous time.     

The exact distribution of indefinite quadratic forms in noncentral normal vectors

The exact density of the difference of two linear combinations of independent noncentral chi-square variables is obtained in terms of Whittaker's function and expressed in closed forms. Two distinct representations are required in order to cover all the possible cases. The corresponding expressions for the exact distribution function are also given.     

求无穷级数的和以及极限的概率方法

文［１］用概率的思想证明了一些不等式和恒等式，本文举例说明概率思想在求无穷级数的和以及极限方面的应用．     

The use of the tetrachoric series for evaluating multivariate normal probabilities

The tetrachoric series is a technique for evaluating multivariate normal probabilities frequently cited in the statistical literature. In this paper we have examined the convergence properties of the tetrachoric series and have established the following. For orthant probabilities, the tetrachoric series converges if $\text{|;?}{}_{\mathrm{ij}}\text{|; <}\text{1}\text{(k ? 1)}$, 1 ≤ i < j ≤ k, where ?_ij are the correlation coefficients of a k-variate normal distribution. The tetrachoric series for orthant probabilities diverges whenever k is even and $\text{?}{}_{\mathrm{ij}}\text{>}\text{1}\text{(k ? 1)}$ or k is odd and $\text{?}{}_{\mathrm{ij}}\text{>}\text{1}\text{(k ? 2)}$, 1 ≤ i < j ≤ k. Other specific results concerning the convergence or divergence of this series are also given. The principal point is that the assertion that the tetrachoric series converges for all k ≥ 2 and all ?_ij such that the correlation matrix is positive definite is false.     

A characterization of multivariate normal distribution and its application

Let X₁, X₂, …, X_n be i.i.d. d-dimensional random vectors with a continuous density. Let and . In this paper we find that the distribution of Z_k (or Y_k) can be used for characterizing multivariate normal distribution. This characterization can be employed for testing multivariate normality in terms of the so-called transformation method.     

A characterization of the multivariate normal distribution

Let X_j (j = 1,…,n) be i.i.d. random variables, and let Y′ = (Y₁,…,Y_m) and X′ = (X₁,…,X_n) be independently distributed, and A = (a_jk) be an n × n random coefficient matrix with a_jk = a_jk(Y) for j, k = 1,…,n. Consider the equation U = AX, Kingman and Graybill [Ann. Math. Statist.41 (1970)] have shown U ～ N(O,I) if and only if X ～ N(O,I). provided that certain conditions defined in terms of the a_jk are satisfied. The task of this paper is to delete the identical assumption on X₁,…,X_n and then generalize the results to the vector case. Furthermore, the condition of independence on the random components within each vector is relaxed, and also the question raised by the above authors is answered.     

On confidence sequences for the mean vector of a multivariate normal distribution

Let X₁, X₂,… be idd random vectors with a multivariate normal distribution N(μ, Σ). A sequence of subsets {R_n(a₁, a₂,…, a_n), n ≥ m} of the space of μ is said to be a (1 − α)-level sequence of confidence sets for μ if P(μ R_n(X₁, X₂,…, X_n) for every n ≥ m) ≥ 1 − α. In this note we use the ideas of Robbins Ann. Math. Statist. 41 (1970) to construct confidence sequences for the mean vector μ when Σ is either known or unknown. The constructed sequence R_n(X₁, X₂, …, X_n) depends on Mahalanobis' or Hotelling's according as Σ is known or unknown. Confidence sequences for the vector-valued parameter in the general linear model are also given.     

Binomial Moments of the Distance Distribution and the Probability of Undetected Error

In [1] K. A. S. Abdel-Ghaffar derives a lower bound on the probability of undetected error for unrestricted codes. The proof relies implicitly on the binomial moments of the distance distribution of the code. We use the fact that these moments count the size of subcodes of the code to give a very simple proof of the bound in Abdel by showing that it is essentially equivalent to the Singleton bound. This proof reveals connections of the probability of undetected error to the rank generating function of the code and to related polynomials (Whitney function, Tutte polynomial, and higher weight enumerators). We also discuss some improvements of this bound.Finally, we analyze asymptotics. We show that an upper bound on the undetected error exponent that corresponds to the bound of Abdel improves known bounds on this function.     

Discrete distributions of orderk on a binary sequence

Summary This paper considers discrete distributions of orderk based on a binary sequence which is defined as an extension of independent trials with a constant success probability and is more practical than the independent trials. Some results on calculation of probabilities and characteristics of the distributions are obtained as well as their formal expressions. Examples and an application are also given. The Institute of Statistical Mathematics