关于二维连续型随机变量线性组合的概率密度

由二维连续型随机变量和的分布的概率密度推广得到二维连续型随机变量线性组合的概率密度.     

两个n维随机变量函数的概率密度的求法

从二维随机变量函数的概率密度的求法出发,引入了n维随机变量函数的概率密度的求法,并介绍了两个常见的n维随机变量函数的概率密度的求法.     

判定二维随机变量服从正态分布的一个充分条件

探讨了二维随机变量服从正态分布的一个充分条件.在两个不相关的随机变量的任意正整数线性组合都是正态随机变量的条件下,利用矩生成函数证明了它们分别服从正态分布,且联合分布也是二维正态分布.     

随机变量独立性的直接判别法

二维离散随机变量相互独立的充要条件是其联合概率矩阵的秩为1；二维连续型随机变量相互独立的充要条件是其联合密度函数可分离变量．     

二维随机变量和谐度量函数及其应用

本文讨论基于二维随机变量递增速度快慢的刻画及描述问题.引入和谐函数的概念,并利用和谐函数对二维随机变量和谐度量指标的定义进行改进,使得对二维随机变量之间和谐关系的研究更进一步.此外,借助于连接函数得到和谐函数的性质和计算公式,并将其应用到具体实例,借助和谐函数的图形进一步验证和谐函数的性质和主要结论.     

随机变量独立性的简易判别法

给出二维随机变量独立性的一个简易判别法，证明其存在的合理性，并将其推广至n维随机变量及其函数独立性的判别．     

二维连续型随机变量函数的分布密度的计算

二维连续型随机变量函数的密度函数的计算既是概率论教学中的一个重点,又是一个难点.本文介绍了一般二维连续型随机变量函数的分布密度的计算方法,并给出了一个新的方法——密度函数转化法.     

基于全概率公式的随机变量边际分布

探讨如何利用全概率公式求二维随机变量的边际分布.特别的对二维连续型随机变量的边际分布进行了形式的推导,并发现积分区域对连续型全概率公式的重要影响.通过几个典型例题着重说明了该公式对积分区域形状的依赖.     

一类由一维概率密度函数构造多维概率密度函数的方法

将文献[1]给出的由一维连续型随机变量的概率密度函数构造二维连续型随机变量的概率密度函数的方法,推广为由一维连续型随机变量的概率密度函数构造三维连续型随机变量的概率密度函数的情况,并作出了证明和举例说明.说明利用本文的方法构造多维概率密度函数,其方法简单易行.     

二维均匀分布的一个简单应用

给出了二维均匀分布在求随机变量函数的分布时的简单应用．     

Central limit theorem for linearly dependent fields of continuous elements

Summary The central limit theorem for stationary linearly dependent sequences is extended for elements in the space of continuous functions on a compact metric space. The proof is based on a new estimate for exponential-type moments of sums of independent random variables.     

On the Skitovich-Darmois theorem and Heyde theorem in a Banach space

According to the well-known Skitovich-Darmois theorem, the independence of two linear forms of independent random variables with nonzero coefficients implies that the random variables are Gaussian variables. This result was generalized by Krakowiak for random variables with values in a Banach space in the case where the coefficients of forms are continuous invertible operators. In the first part of the paper, we give a new proof of the Skitovich-Darmois theorem in a Banach space. Heyde proved another characterization theorem similar to the Skitovich-Darmois theorem, in which, instead of the independence of linear forms, it is supposed that the conditional distribution of one linear form is symmetric if the other form is fixed. In the second part of the paper, we prove an analog of the Heyde theorem in a Banach space. Translated from Ukrains'kyi Matematychnyi Zhurnal, Vol. 60, No. 9, pp. 1234–1242, September, 2008.     

A non-central banach valued limit theorem and its application

This note is devoted to the generalization of a limit theorem on sums of strongly dependent random variables (non-central limit theorem) to the case when the initial Gaussian sequence, functions of which are considered, assumes values in the Banach space of continuous functions on a compactum; special attention is given to the transition case, when the limit distribution changes by a jump.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova Akademii Nauk SSSR, Vol. 177, pp. 114–119, 1989.     

Approximation and extension of random functions

Stochastic versions of the extension theorems of Tietze and Dugundji are obtained, as well as an existence theorem for partitions of unity by random continuous functions. A form of the classical approximation theorem of Mergelyan valid for random holomorphic functions on random compact sets is presented. A similar approach yields versions of the approximation theorems of Runge, Arakelyan, and Vitushkin.Research of both authors was partially supported by the NSF under Grant No. DMS 85-02308     

关于加权不变原理的一个注记

在这个注记中我们将关于平稳过程的Davydov弱不变原理推广到长记忆无穷滑动平均过程的加权部分和过程,文中还给出了一些不限于滑动平均过程的一般长记忆时间序列的加权部分和过程增量的二阶矩的边界,这些边界将有助于证明这些过程关于一致度量的胎紧性.作为连续映射定理的一个结果, 我们也导出了一些随机变量函数的概率边界.     

The strong law of large numbers for L-statistics with dependent data

We prove the strong law of large numbers for the linear combinations of functions of order statistics (L-statistics) based on weakly dependent random variables. We also establish the Glivenko-Cantelli theorem for ?-mixing sequences of identically distributed random variables.     

Sufficient conditions for Benford’s law

We present two sufficient conditions for an absolutely continuous random variable to obey Benford’s law for the distribution of the first significant digit. These two sufficient conditions suggest that Benford’s law will not often be observed in everyday sets of numerical data. On the other hand, we recall that there are two processes by way of which a random variable can come close to following Benford’s law. The first of these is the multiplication of independent random variables and the second is the exponentiation of a random variable to a large power. Our working tool is the Poisson sum formula of Fourier analysis. Like the central limit theorem, Benford’s law has an asymptotic nature.     

Series representations for densities functions of a family of distributions—Application to sums of independent random variables

Series representations for several density functions are obtained as mixtures of generalized gamma distributions with discrete mass probability weights, by using the exponential expansion and the binomial theorem. Based on these results, approximations based on mixtures of generalized gamma distributions are proposed to approximate the distribution of the sum of independent random variables, which may not be identically distributed. The applicability of the proposed approximations are illustrated for the sum of independent Rayleigh random variables, the sum of independent gamma random variables, and the sum of independent Weibull random variables. Numerical studies are presented to assess the precision of these approximations.     

对数似然比与离散随机变量序列强极限定理的一种分析方法

本文引进对数似然比作为任意离散随机变量序列相依性的一种度量，并通过限制似然比给出样本空间的某种子集，在这种子集上得到了离散随机变量序列的一类强极限定理，它包含若干经典强大数定律为其特例．在证明中本文提出了证明强极限定理的一种分析方法，其要点是将关于单调函数可微性的定理应用于几乎处处收敛的研究．