对称随机变量序列的收敛性

讨论了对称的随机变量序列的完全收敛性与强大数律,改进和加强了独立同分布时Baum L E,Katz M及Bai Z D,Cheng P E相应的结果.     

AANA随机变量序列的中心极限定理

研究了渐近几乎负相依(简称为AANA)随机变量序列的渐近正态问题.在非常一般的条件下,得到了AANA序列的中心极限定理,推广了负相依(简称为NA)、独立随机变量序列的相应结论.     

整值随机变量序列发生次数与条件概率和的等价性

在没有独立性、平稳性和相依性假设的条件下,利用分析方法讨论了整值随机变量序列取某个数值的次数与随机变量序列取该数值的条件概率和的等价性问题,建立并证明了若干强极限定理.     

独立随机变量序列加权和的相合性

设t0∈(0，1)，Wni(t0)是关于实变量t1，t2，…，tn的权函数；随机变量序列Y1，Y2，…，Yn，iid．本研究了随机变量序列加权和∑(i=1,n)Wni(t0)Yi的相合性．     

ρ*-混合随机变量序列部分和的方差估计

本文研究了ρ^＊-混合随机变量序列部分和的方差估计，给出了部分和方差估计的相合性及渐近正态性．     

整值随机变量序列的一类强律

本文引进似然比作为整值随机变量序列相对于服从Poisson分布的独立随机变量序列的偏差的一种度量，并通过限制似然比给出了样水空间的某种子集．在这种子集上得到了一类用不等式表示的强律，独立随机变量序列的一类强律是其特例．     

离散随机变量序列的一类强律

本文引进似然比作为离散随机变量序列相依性的一种度量，并利用这个概念给出一类在样本空间的某种子集上的强律，它包含若干通常的强大数定律为其特例。     

随机变量的截尾与任意随机变量序列的强极限定理

利用随机变量的截尾研究任意随机变量序列的性质,建立了一类矩条件下任意随机变量序列的强极限定理.作为推论,得到了可列非齐次马尔可夫过程的一个强极限定理,推广了鞅差序列当1≤p≤2和p≥2时的Chow定理,相应的一些已有结果和若干经典的关于独立随机变量序列的强大数定律是本文的特例。     

WOD随机变量序列加权和的矩完全收敛性

WOD(widely orthant dependent)随机变量序列是一类非常宽泛的相依随机变量序列,应用WOD随机变量序列部分和的Menshov-Rademacher型不等式,结合五段截尾技术,研究得到了同分布WOD随机变量序列的Sung型加权和的最大值矩完全收敛性定理,推广和改进了已有的文献的一些最新结果.作为主要结果的一个应用,同时考虑了基于WOD误差的非参数回归模型中加权估计的完全相合性的结果,并且通过模拟验证了理论结果的有效性.     

随机变量序列两种收敛性的一个注记

随机变量序列的依概率收敛必有依分布收敛,反之未必成立.若极限随机变量服从退化分布,则两种收敛等价.自然地,若这两种收敛等价,是否有极限随机变量服从退化分布?给予该问题肯定的回答.     

Further consideration on normal random variable generator

Summary In a previous paper [3] the author considered a computer technique for generating exponential random variables and its applications to the generation of random variables of other types. He suggested to combine the new exponential random variable generator with J. C. Butcher’s method [1] to get normal random deviates. In this note, an improvement of the idea is shown with a remark on the rejection technique in the Monte Carlo method.     

Behavior of random walks on Z in Gibbsian medium

We consider random walks on $\text{Z}$ in a random medium with {?L,…,?1,0,+1} as possible jumps, where L?1 is fixed. When the environment is defined by a Gibbs measure on a subshift of finite type, we show a dichotomy in the recurrent case between the pointwise functional CLT and the slow behavior described by Sinaï. In the transient cases and under natural integrability conditions, we prove the validity of the averaged CLT. To cite this article: J. Bremont, C. R. Acad. Sci. Paris, Ser. I 338 (2004).     

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．     

On exponential and other random variable generators

G. Marsaglia [6] proposed a new method for generating exponential random variables. In this note, his method is modified and generalized for generating χ² random variables with even degrees of freedom. Remarks refer to general χ² and normal random variable generators.     

Tail probability of random variable and Laplace transform

We investigate the exponential decay of the tail probability P(X?>?x) of a continuous type random variable X. Let ?(s) be the Laplace–Stieltjes transform of the probability distribution function F(x)?=?P(X?≤?x) of X, and σ₀ be the abscissa of convergence of ?(s). We will prove that if ?∞?0?s) on the axis of convergence are only a finite number of poles, then the tail probability decays exponentially. For the proof of our theorem, Ikehara's Tauberian theorem will be extended and applied.     

离散随机变量序列的强偏差定理

利用分析方法建立了用不等式表示的用对数似然比刻划的任意相依离散随机变量序列的强偏差定理,作为推论得到了更一般的离散随机变量序列加权和的强大数定律.     

Density minoration of a strongly non-degenerated random variable

We obtain a lower bound for the density of a d-dimensional random variable on the Wiener space under exponential moment condition of the divergence of covering vector fields.     

A stopping rule for a compound poisson random variable

An optimal empirical Bayesian stopping rule for the Poisson compounded with the geometric distribution is developed and applied to the problem of the sequential testing of computer software. For each checkpoint in time, either the software satisfies a desired economic criterion, or else the software testing is continued.     

Sequential estimation of the minimum of a random variable

The paper presents a sequential method of fixed-width asymptotically consistent estimation of the minimal value μ of a random variable in the sense μ = inf{ x: F(x) > 0} where F is a distribution function of the considered variable. The theory of extremal order statistics is used.