应用曲线积分求解条件概率密度函数

本文将随机变量X在随机事件■(其中f(X)为线性函数)的条件下的概率分布转化为联合密度函数在该曲线段上的曲线积分,研究了在随机事件B发生的条件下随机变量X的条件概率密度函数.同时,本文通过例证说明,通过曲线积分求解条件概率密度函数较之从条件分布定义的求解方式更为简洁.     

条件概率空间上随机变量的分布

利用一般概率空间中随机变量的联合密度函数与边缘密度函数表示条件概率空间上随机变量的分布及随机变量的联分合布、边缘分布与和、差、积、商的分布。     

依概率收敛与依分布收敛的关系

本探讨了随机变量序列依概率收敛与依分布收敛的关系,并给出了一个依分布收敛能保证依概率收敛的最弱的条件,即：设分布函数列{Fn（x）}弱收敛于连续的分布函数F(x),则存在随机变量序列{ξn}和随机变量ξ,它们分别以{Fn（x）}和F（x）为其对应的分布函数和分面函数,且{ξn}依概率收敛于ξ。     

概率分布的实函数实现问题

基于一维随机变量,通过阐释概率分布实函数实现的内在本质,给出了概率分布实函数实现的一个充分必要条件,得到分布函数族及其连续性特征,揭示出概率论中分布函数定义所蕴含的合理性和深刻性.     

依概率收敛与依分布收敛的关系

本文探讨了随机变量序列依概率收敛与依分布收敛的关系 ,并给出了一个依分布收敛能保证依概率收敛的最弱的条件 ,即 :设分布函数列 { Fn(x) }弱收敛于连续的分布函数 F(x) ,则存在随机变量序列{ξn}和随机变量ξ,它们分别以 { Fn(x) }和 F(x)为其对应的分布函数列和分布函数 ,且 {ξn}依概率收敛于ξ.     

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.     

n元随机变量联合分布的分解

定义和研究了函数Sum(n，t)，并借助于此以模n剩余类环上函数的频谱理论为工具解决了n元随机变量联合分布的分解问题。     

统计学上三大分布推导方法

讨论了如何求随机变量函数分布的方法,然后用两种方法推出统计学上三个重要分布的概率分布密度函数.方法独特新颖.     

多维对称随机变量分布函数的充要条件

讨论了多维对称随机变量的若干性质,阐明其分量仍是对称随机变量,在此基础上,进一步给出多维对称随机变量分布函数的一个充分必要条件.     

随机误差的分布机理

研究随机变量误差的分布函数,通过数理变形、推导证明此随机变量精确服从正态分布.     

条件期望的两种定义及其等价性探讨

讨论随机变最在给定子σ代数下条件期望的定义,利用投影定理这一数学工具给出条件期望的几何定义,并通过对它与现今各种概率论基础或随机过程教材中常见的公理化定义相互等价性的证明,揭示了条件期望这一概念的内涵.     

基于二维t分布的条件VaR研究

根据多元t分布的定义及性质,推导出二维t分布随机变量差的条件分布仍服从t分布.在假定股票价格对数与收益率服从二维t分布的基础上,利用该性质,可以得到不同股票价格水平条件下,收益率的一维条件t分布,进而计算出价格条件VaR.利用多元t分布研究价格条件的收益率分布问题,与正态分布相比,较好地刻画了证券收益率分布的尖峰厚尾现象.     

Estimating Some Characteristics of the Conditional Distribution in Nonparametric Functional Models

This paper deals with a scalar response conditioned by a functional random variable. The main goal is to estimate nonparametrically some characteristics of this conditional distribution. Kernel type estimators for the conditional cumulative distribution function and the successive derivatives of the conditional density are introduced. Asymptotic properties are stated for each of these estimates, and they are applied to the estimations of the conditional mode and conditional quantiles. Our asymptotic results highlightes the importance of the concentration properties on small balls of the probability measure of the underlying functional variable. So, a special section is devoted to show how our results behave in several situations when the functional variable is a continuous time process, with special attention to diffusion processes and Gaussian processes. Even if the main purpose of our paper is theoretical, an application to some chemiometrical data set coming from food industry is presented in a short final section. This example illustrates the easy implementation of our method as well as its good behaviour for finite sample sizes.     

On the compounded bivariate Poisson distribution: A unified treatment

A unified treatment is presented here of compounding with the bivariate Poisson distribution. Exploiting the exponential nature of its probability generating function, it is shown that the pgf of the compound distribution is the moment generating function of the compounding random variable. This relationship leads to rather interesting general results. Particularly, the development of the conditional distribution is simplified. Four cases are presented in detail.     

模糊概率随机变量

研究了第二类模糊随机变量-具有清晰事件，模糊概率的随机变量的数学描述。在区间概率的基础上。利用模糊分解定理给出了概率模糊数集是可行的条件。进一步给出院 具有模糊概率的随机变量及模糊概率随机变量的模糊分布函数和模糊分布列的定义和性质。提出并证明了具有模糊概率运算封闭性的模糊概率分解定理。研究了模糊概率随机变量的模糊数学期望和模糊方差的定义和性质。所有关于模糊概率随机变量的数学描述都具有模糊概率运算的封闭性。这为完善模糊概率的运算方法打下了基础。     

Second-order nonlinear least squares estimation

The ordinary least squares estimation is based on minimization of the squared distance of the response variable to its conditional mean given the predictor variable. We extend this method by including in the criterion function the distance of the squared response variable to its second conditional moment. It is shown that this “second-order” least squares estimator is asymptotically more efficient than the ordinary least squares estimator if the third moment of the random error is nonzero, and both estimators have the same asymptotic covariance matrix if the error distribution is symmetric. Simulation studies show that the variance reduction of the new estimator can be as high as 50% for sample sizes lower than 100. As a by-product, the joint asymptotic covariance matrix of the ordinary least squares estimators for the regression parameter and for the random error variance is also derived, which is only available in the literature for very special cases, e.g. that random error has a normal distribution. The results apply to both linear and nonlinear regression models, where the random error distributions are not necessarily known.     

模糊随机变量及其概率分布

本文在模糊σ－代数及模糊数的极大全序子集之上定义了模糊随机变量，进而首次定义了客观实用的模糊随机变量的概率分布函数，并讨论了数学期望及方差等数字特征。     

Characterizations of discrete distributions by a conditional distribution and a regression function

Summary The bivariate distribution of (X, Y), whereX andY are non-negative integer-valued random variables, is characterized by the conditional distribution ofY givenX=x and a consistent regression function ofX onY. This is achieved when the conditional distribution is one of the distributions: a) binomial, Poisson, Pascal or b) a right translation of these. In a) the conditional distribution ofY is anx-fold convolution of another random variable independent ofX so thatY is a generalized distribution. A main feature of these characterizations is that their proof does not depent on the specific form of the regression function. It is also indicated how these results can be used for good-ness-of-fit purposes.     

IIRCT下泊松分布参数多变点模型的贝叶斯估计

通过添加缺损的寿命变量数据得到了带有不完全信息随机截尾试验下泊松分布参数多变点模型的完全数据似然函数,研究了变点位置参数和其它参数的满条件分布.利用Gibbs抽样与Metropolis-Hastings算法相结合的MCMC方法对各参数的满条件分布分别进行了抽样,把Gibbs样本的均值作为各参数的贝叶斯估计,并且详细介绍了MCMC方法的实施步骤.最后进行了随机模拟试验,试验结果表明各参数贝叶斯估计的精度都较高.     

删失截断情形下失效率变点模型的Bayes参数估计

通过添加部分缺失寿命变量数据,得到了删失截断情形下失效率变点模型相对简单的似然函数.讨论了所添加缺失数据变量的概率分布和随机抽样方法.利用Monte Carlo EM算法对未知参数进行了迭代.结合Metropolis-Hastings算法对参数的满条件分布进行了Gibbs抽样,基于Gibbs样本对参数进行估计,详细介绍了MCMC方法的实施步骤.随机模拟试验的结果表明各参数Bayes估计的精度较高.