宽相依随机变量加权和的强极限定理

Strong limit theorems are established for weighted sums of widely orthant dependent（WOD） random variables. As corollaries, the strong limit theorems for weighted sums of extended negatively orthant dependent（ENOD） random variables are also obtained, which extend and improve the related known works in the literature.     

相依随机变量和的极限理论

本文对各种混合r.w.序列及相伴r.w.序列的有关结果作一概略介绍。     

相依随机变量列的随机和的中心极限问题

本文给出阵列框架下的相依随机变量列的随机和弱收敛和稳定收敛的充分必要条件，我们的结果与经典结果相似并囊括此方向上的所有已知结果．     

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.     

线性模型误差方差估计中心极限定理余项的非一致估计

<正> 以 F_n(x)和φ(x)分别记((?)_n~2-σ~2)/(?)和标准正态随机变量的分布函数.关于 F_n(x)收敛于φ(x)的速度问题,一直是人们关心的问题.文献[1],[2]证明了δ=1时的一致和非一致收敛速度的估计问题,达到了与独立和同样好的界限.本文则证明了如下的结果:     

α混合随机变量序列部分和乘积的几乎处处中心极限定理

利用子序列等方法,获得α混合随机变量序列部分和乘积的几乎处处中心极限定理的更优结果,改进了相关文献的结果.     

相依序列加权和的几乎处处中心极限定理

该文讨论了非平稳负(正)相依序列加权和的几乎处处中心极限定理,改进并推广了相依序列几乎处处中心极限定理的相关结果.     

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．     

相依二值随机变量序列游程的一类强极限定理

本文利用文献[1]与[2]中提出的方法和思想,研究相依二值随机变量序列游程的极限性质,得到游程平均数的一类强极限定理.     

Self-normalized central limit theorem for sums of weakly dependent random variables

Let {X _n,n1} be a strictly stationary sequence of weakly dependent random variables satisfyingEX _n=,EX _n ² <,Var S _n /n² and the central limit theorem. This paper presents two estimators of ². Their weak and strong consistence as well as their rate of convergence are obtained for -mixing, -mixing and associated sequences.Supported by a NSF grant and a Taft travel grant. Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio 45221-0025.Supported by a Taft Post-doctoral Fellowship at the University of Cincinnati and by the Fok Yingtung Education Foundation of China. Hangzhou University, Hangzhou, Zhejiang, P.R. China and Department of Mathematics, National University of Singapore, Singapore 0511.     

A central limit theorem for random sums of random variables

Anscombe (1952) (also see Chung (1974)) has developed a central limit theoremof random sums of independent and identically distributed random variables. Applicability of this theorem in practice, however, is limited since the normalization requires random factors. In this paper we establish sufficient conditions under which the central limit theorem holds when such random factors are replaced by the underlying asymptotic mean and standard ddeviation. An application of this result in the context of shock models is also given.     

Almost sure central limit theory for products of sums of partial sums

Consider a sequence of i.i.d. positive random variables. An universal result in almost sure limit theorem for products of sums of partial sums is established.We will show that the almost sure limit the...     

An almost sure central limit theorem for products of sums of partial sums under association

Let be a strictly stationary positively or negatively associated sequence of positive random variables with EX₁=μ>0, and VarX₁=σ²<∞. Denote , and γ=σ/μ the coefficient of variation. Under suitable conditions, we show that

     

Almost sure central limit theorem for partial sums and maxima

Let X, X₁, X₂, … be i.i.d. random variables with nondegenerate common distribution function F, satisfying EX = 0, EX² = 1. Let X_i and M_n = max{X_i, 1 ≤ i ≤ n }. Suppose there exists constants a_n > 0, b_n ∈ R and a nondegenrate distribution G (y) such that Then, we have almost surely, where f (x, y) denotes the bounded Lipschitz 1 function and Φ(x) is the standard normal distribution function (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Central limit theorem and almost sure central limit theorem for the product of some partial sums

In this paper, we give the central limit theorem and almost sure central limit theorem for products of some partial sums of independent identically distributed random variables.     

离散随机序列加权和的若干极限定理

设{Xn,n≥0}是任意离散随机变量序列,{ank,0≤k≤n,n≥0)是一常数阵列,我们引入随机序列渐近对数似然比的概念,作为表征随机序列的真实概率测度P与参考测度Q之间的差异的度量,用分析方法,得到了随机序列Jamison型加权和的若干随机偏差定理.