关于二维连续型随机变量独立性的判断

随机变量的独立性是概率论中最基本的概念之一，通过对它的研究可使一些实际问题的概率模型的具体计算得到简化。因而，关于随机变量的独立性的研究构成了概率的重要课题。本文仅就二维连续型随机变量，给出了两种判断其分量独立性的理论和具体方法，并对其进行了比较。     

随机变量独立性的判别方法

归纳总结判别随机变量独立性的方法,并借助实例加以说明.     

分布矩阵与随机变量独立性的判定

利用离散型随机变量的联合分布矩阵,得到了离散型随机变量独立性的一种判别方法,并用实例给出了一定的应用。     

条件数学期望与随机变量独立性的一个充要条件

随机试验的独立性、随机事件的独立性、随机变量的独立性均是概率统计中的重要概念,不少学者都在这些方面有所讨论.本文作者就二维离散形随机向量(ξ,η)中两个分量ξ与η的相互独立性展开讨论.先是证明了三个引理,其中引理1在一般概率论教科书中均有介绍,但为使读者方便,作者也作了证明.利用三个引理,作者找到随机变量独立性的一个充要条件.     

两种独立性定义的等价性

随机变量独立性的定义,一般由随机变量的分布函数来刻画.但在实际应用中,却通常使用其等价条件来判断随机变量是否独立.本文分别针对连续型和离散型两类随机变量,论证了这两种独立性概念之间的等价性.     

关于正态随机变量的独立性与相关性

关于正态随机变量的独立性与相关性傅自晦（烟台大学数学与信息科学系，山东烟台２６４００５）众所周知，两个随机变量（向量）独立，则一定不相关，而不相关不一定独立；但不少初学者却误以为两个正态随机变量（向量）的独立性与不相关性是等价的，并由此导出一些理论上...     

Pr值逻辑函数相关免疫的等价判别条件

本文首先基于环Zpr中的元的padic分解并结合概率论的思想,给出了pr值随机变量的分解性质及pr值随机变量独立性的等价描述,然后在对pr值逻辑函数及其变元都进行padic分解的基础上,直接通过p值逻辑函数的Chrestenson谱给出了padic分解意义下pr值逻辑函数k阶相关免疫的线性组合引理和谱判别定理.     

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 .     

《概率论与数理统计》中有关独立性一反例的深入分析

独立性是《概率论与数理统计》是的一个非常重要的概念.教学中在说明随机变量函数独立性时会涉及许多反例.本文就有关随机变量函数独立性的一个反例作了进一步的推广分析.     

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．     

离散型随机变量的几种独立性及相互关系

随机变量的独立性在概率论中有着十分重要的意义.本文给出了离散型随机变量与离散型随机向量相互独立的概念,条件独立的概念,以及几种独立性的相互关系.     

The almost equivalence of pairwise and mutual independence and the duality with exchangeability

For a large collection of random variables in an ideal setting, pairwise independence is shown to be almost equivalent to mutual independence. An asymptotic interpretation of this fact shows the equivalence of asymptotic pairwise independence and asymptotic mutual independence for a triangular array (or a sequence) of random variables. Similar equivalence is also presented for uncorrelatedness and orthogonality as well as for the constancy of joint moment functions and exchangeability. General unification of multiplicative properties for random variables are obtained. The duality between independence and exchangeability is established through the random variables and sample functions in a process. Implications in other areas are also discussed, which include a justification for the use of mutually independent random variables derived from sequential draws where the underlying population only satisfies a version of weak dependence. Macroscopic stability of some mass phenomena in economics is also characterized via almost mutual independence. It is also pointed out that the unit interval can be used to index random variables in the ideal setting, provided that it is endowed together with some sample space a suitable larger measure structure. Received: 16 April 1997 / Revised version: 18 May 1998     

Matricially free random variables

We show that the framework developed by Voiculescu for free random variables can be extended to arrays of random variables whose multiplication imitates matricial multiplication. The associated notion of independence, called matricial freeness, can be viewed as a concept which not only leads to a natural generalization of freeness, but also underlies other fundamental types of noncommutative independence, such as monotone independence and boolean independence. At the same time, the sums of matricially free random variables, called random pseudomatrices, are closely related to random matrices. The main results presented in this paper concern the standard and tracial central limit theorems for random pseudomatrices and the corresponding limit distributions which can be viewed as matricial semicircle laws.     

WEAK UNCORRELATEDNESS OF RANDOM VARIABLES

New measures of independence for n random vaxiables, based on their moments, are studied. A scale of degrees of independence for random variables which starts with uncorrelatedness (for n = 2) and finishes at independence is constructed. The scale provides a countable linearly ordered set of measures of independence.     

Multidimensional dependency measures

The problem of dependency between two random variables has been studied throughly in the literature. Many dependency measures have been proposed according to concepts such as concordance, quadrant dependency, etc. More recently, the development of the Theory of Copulas has had a great impact in the study of dependence of random variables specially in the case of continuous random variables. In the case of the multivariate setting, the study of the strong mixing conditions has lead to interesting results that extend some results like the central limit theorem to the case of dependent random variables.In this paper, we study the behavior of a multidimensional extension of two well-known dependency measures, finding their basic properties as well as several examples. The main difference between these measures and others previously proposed is that these ones are based on the definition of independence among n random elements or variables, therefore they provide a nice way to measure dependency.The main purpose of this paper is to present a sample version of one of these measures, find its properties, and based on this sample version to propose a test of independence of multivariate observations. We include several references of applications in Statistics.     

A Nonparametric Test of Serial Independence for Time Series and Residuals

This paper presents nonparametric tests of independence that can be used to test the independence of p random variables, serial independence for time series, or residuals data. These tests are shown to generalize the classical portmanteau statistics. Applications to both time series and regression residuals are discussed.     

Stability of sums of weighted nonnegative random variables

A stability result for sums of weighted nonnegative random variables is established and then it is utilized to obtain, among other things, a slight generalization of the Borel-Cantelli lemma and to show that the work of Jamison, Orey, and Pruitt (Z. Wahrsch. Verw. Gebiete4 (1965), 40–44) on almost sure convergence of weighted averages of independent random variables remains valid if the assumption of independence on the random variables is replaced by pairwise independence.     

三变量独立性的两个结论

本文给出了三随机变量相互独立与条件独立的两个结论．其中结论２表明，三变量如果两两条件独立，则三变量一定相互独立．