i．i．d随机变量随机和的数字特征

讨论了i．i．d的随机变量随机和的数学期望和方差、特征函数以及矩母函数的计算公式。并对求和个数服从几何分布和Possion分布，随机变量服从指数分布时给出了直接结果．     

相互独立的m值随机变量和的极限分布定理

利用ｍ值随机变量的特征函数,在一定条件下,得到了相互独立的ｍ值随机变量和的极限分布均匀的充要条件;再结合无穷乘积的有关性质,给出了相互独立的ｍ值随机变量和极限分布均匀分布的充分条件,特别当ｍ为素数Ｐ时,所得的充分条件易于验证,且不难满足。     

均匀分布的一个性质及其应用

运用特征函数的连续性定理和数学归纳法,证明了随机变量服从[0,1]上的均匀分布的充要条件是,它可以用二进制形式表示成独立同伯努利分布的随机变量序列的无穷级数.给出了此结论在生成随机数中的运用,以及在证明独立同分布序列存在性定理中的运用.     

一般变量的随机Taylor级数

研究了随机Taylor级数的值分布,用不同于Kahane的方法先证明了一个关于特征函数增长性的定理,然后利用它证明一般的随机变量级数的值域在复平面上几乎必然是处处稠密的;若是有界连续型随机变量级数,它还几乎必然没有有限Nevanlinna亏值.     

椭球过程的导数与积分的特征函数

令X(t)为椭球随机变量,我们引入椭球过程的概念。在椭球过程的导数与积分存在的条件下,本文给出了椭球过程的导数与积分的特征函数。     

随机Taylor级数

该文对一般的随机变量序列及相当弱的系数条件研究了随机级数定义的整函数的奈望里纳特征函数，并证明了它是几乎必然无有限例外值的．     

受延迟随机损伤系统可修复时间的分布特征与修复概率

研究受延迟随机损伤系统可修复时间的概率特性．即系统初始运行安全期时间长度为一随机变量,受Poisson过程规律的随机冲击并产生随机损伤．用条件随机过程和条件Markov过程为数学工具,求出系统可修复时间的密度函数与特征函数以及可修复概率．     

离散型与连续型分布互为对偶的根源

互为对偶的离散型分布与连续型分布,可以看作是由同一个函数——源函数产生的。源函数的正线性组合、乘积和负导数,仍然是源函数。源函数揭示了互为对偶的分布的分布函数之间的相互关系,并能用来求随机变量的数字特征、特征函数、概率母函数、分布的最大值和参数的极大似然估计.     

随机波动率与跳扩散组合模型的双币种期权定价

在股价和汇率满足随机波动率与跳扩散组合模型下应用半鞅Ito公式、多维随机变量的联合特征函数、Girsanov测度变换以及Fourier反变换等随机分析技巧给出了双币种欧式期权价格的封闭式解,并利用数值实例分析了波动参数对期权价格的影响,结果表明:波动率参数对期权价格有显著的影响作用.     

特征函数的性质

得到了特征函数、条件特征函数的一致可微性和条件特征函数的反演公式.     

On the Distribution of the Sum of n Non-Identically Distributed Uniform Random Variables

The distribution of the sum of independent identically distributed uniform random variables is well-known. However, it is sometimes necessary to analyze data which have been drawn from different uniform distributions. By inverting the characteristic function, we derive explicit formulae for the distribution of the sum of n non-identically distributed uniform random variables in both the continuous and the discrete case. The results, though involved, have a certain elegance. As examples, we derive from our general formulae some special cases which have appeared in the literature.     

A class of models for uncorrelated random variables

We consider the class of multivariate distributions that gives the distribution of the sum of uncorrelated random variables by the product of their marginal distributions. This class is defined by a representation of the assumption of sub-independence, formulated previously in terms of the characteristic function and convolution, as a weaker assumption than independence for derivation of the distribution of the sum of random variables. The new representation is in terms of stochastic equivalence and the class of distributions is referred to as the summable uncorrelated marginals (SUM) distributions. The SUM distributions can be used as models for the joint distribution of uncorrelated random variables, irrespective of the strength of dependence between them. We provide a method for the construction of bivariate SUM distributions through linking any pair of identical symmetric probability density functions. We also give a formula for measuring the strength of dependence of the SUM models. A final result shows that under the condition of positive or negative orthant dependence, the SUM property implies independence.     

Formula for the Laplace transform of the projection of a distribution on the positive semiaxis and some of its applications

We obtain a formula for the Laplace transform of the restriction of an arbitrary probability distribution on the positive semiaxis in the form of a Cauchy-type integral in infinite limits of the characteristic function of this distribution. Using this result and the estimates of the concentration function of the sum of independent random variables, we derive a representation for the Laplace transform of the restriction of the harmonic measure on the positive semiaxis. In conclusion, we present an estimate of the lower ladder height distribution for the case in which the distribution of the value of the jump in a random walk is normal.     

Some Asymptotic Results for One-Sided Large Deviation Probabilities

We investigate the asymptotic behavior of the sum of independent real random variables. We assume that the random variables are not identically distributed but the average of distribution functions of these random variables is equivalent to some heavy-tailed limit distribution function. An example with Pareto law as limit function is given.     

渐近正态随机变量函数的极限分布

基于渐近正态随机变量,导出随机变量函数极限分布的两个一般性理论结果.作为应用,证明了渐近正态随机变量一系列具体函数的极限分布,其中包括泊松随机变量平方根的渐近正态性,以及随机变量部分和在正则化常数是随机变量情况下的渐近正态性.     

Asymptotic expansions for potential functions of i.i.d. random fields

Summary For sums of finite range potential functions of an iid random field we derive the validity of formal expansions of length two. Under standard conditions, formal expansions are valid if and only if the characteristic functions of the sum converge to zero for all nonzero frequency parameters. If this convergence fails, the distribution of the sum can be approximated by a mixture of lattice distributions. The result applies to m-dependent random fields generated by independent random variables.     

Characterizing a comonotonic random vector by the distribution of the sum of its components

In this article, we characterize comonotonicity and related dependence structures among several random variables by the distribution of their sum. First we prove that if the sum has the same distribution as the corresponding comonotonic sum, then the underlying random variables must be comonotonic as long as each of them is integrable. In the literature, this result is only known to be true if either each random variable is square integrable or possesses a continuous distribution function. We then study the situation when the distribution of the sum only coincides with the corresponding comonotonic sum in the tail. This leads to the dependence structure known as tail comonotonicity. Finally, by establishing some new results concerning convex order, we show that comonotonicity can also be characterized by expected utility and distortion risk measures.     

马氏环境中马氏链的一个概率不等式及其应用

本文研究了马氏环境中马氏链构成的随机变量之和的概率不等式问题.利用了结尾的方法,获得了马氏环境中马氏链构成的随机变量之和的尾部概率不等式,作为结果的应用,给出了将过程限制在(S,S∩F,PS)上的强大数定律.文中提出的方法和结果对研究独立的随机变量之和的大样本性质是十分有用的.     

随机变量的和、积、商分布的一种新表示

应用Feller提出的点-集函数并结合二元copula,对二元连续型正值随机变量的和、积、商的分布进行了研究,得到了和、积、商分布的一种新的计算方法.最后给出一个应用实例.