‖S_n‖超越ε(nlgn)~(1/2)的次数、最大程度和最后时刻的矩的讨论

设{X_n,n≥1}是i.i.d.的B值随机变量序列,本文讨论了‖S_(?)‖超越ε(nlgn)~(1/2)的次数L(ε)、最大程度N(ε)和最后时刻H(ε)关于某一类特定的函数φ(t)的某种形式的矩同相应的尾概率级数收敛性之间的关系,得到了若干等价性命题.     

具有不同分布NA随机变量列的强大数律及应用

给出了具有不同分布的NA随机变量列满足的若干强大数律;作为应用,不仅将独立随机变量的一类强极限定理完整的推广到NA随机变量情形,而且关于NA随机变量的一些已有结果可以作为推论得出.     

NSD随机变量序列的完全收敛性

本文利用NSD随机变量序列的性质、矩不等式及三级数定理,在一定的矩条件下,得到了NSD随机变量序列的完全收敛性.所得结果推广了 Chow与Lai[15,16]与Jajte[17]的独立序列的结果.     

一般矩条件下相依随机变量Sung型加权和的完全收敛性

在一般矩条件下,本文得到了END随机变量和PNQD随机变量的Sung型加权和的完全收敛性定理,这些定理推广了已知的一些文献中相应的结果.     

任意随机变量序列泛函及其随机变换的一类极限定理

采用鞅方法研究对任意随机变量序列普遍成立的强极限定理.并作为推论得到了m阶马氏过程,鞅序列,鞅差序列,独立随机变量序列的一类强极限定理.并把赌博系统的随机变换概念推广到任意随机变量序列的情况,得到任意随机变量序列随机选择与公平比的若干极限定理.     

NA及LNQD随机变量列的几乎处处中心极限定理

本文在二阶矩存在的条件下,证明了NA及LNQD随机变量列的几乎处处中心极限定理,使主要结果成立,其中W为[0,1]上标准Brown运动。     

N值随机变量序列的AEP型极限及若干强偏差定理

设{X_n,n≥1}是在S={1,2,…,N}中取值的随机变量序列,其分布为p(x_1,…,x_n),liminf[P(X_1,…,X_n)]~(1/n)与limsup[p(X_1,…,X_n)]~(1/n)称为AEP型极限。利用这些极限该文得到{X_n,n≥1}的若干强偏差定理,即一类用不等式表示的强极限定理。     

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.     

正相依随机变量的不变原理

对于最大协方差系数以多项式速率下降的正相依随机变量，Birkel给出了矩的上界估计．利用这一结果，Birkel建立了一个泛函中心极限定理．本文首先在最大协方差系数以对数速率下降的条件下给出了相同的矩的上界估计．进一步，基于这个较弱的条件，证明了Birkel的结论仍然成立．     

关于独立随机变量序列强极限定理的注记

构造出一个独立随机变量序列的例子,说明Shiryaev的一个关于独立随机变量序列强极限定理是一个新的类型的定理.     

The Convergence of the Sums of Independent Random Variables Under the Sub-linear Expectations

Let {Xn;n≥1} be a sequence of independent random variables on a probability space(Ω,F,P) and Sn=∑k=1n Xk.It is well-known that the almost sure convergence,the convergence in probability and the convergence in distribution of Sn are equivalent.In this paper,we prove similar results for the independent random variables under the sub-linear expectations,and give a group of sufficient and necessary conditions for these convergence.For proving the results,the Levy and Kolmogorov maximal inequalities for independent random variables under the sub-linear expectation are established.As an application of the maximal inequalities,the sufficient and necessary conditions for the central limit theorem of independent and identically distributed random variables are also obtained.     

NA序列的重对数矩收敛的精确渐近性

假设{X_n,n≥1}为一列严平稳的NA随机变量,期望为零,方差有限.设S_n=∑_(i=1)~n∑X_i,M_n=max_(1≤i≤n)|S_i|.在适当的条件下,得到了一类NA序列部分和部分和最大值重对数矩收敛的精确渐近性.     

ASYMPTOTIC NORMALITY OF KERNEL ESTIMATES OF A DENSITY FUNCTION UNDER ASSOCIATION DEPENDENCE

Let {X_n, n≥1} be a strictly stationary sequence of random variables, whichare either associated or negatively associated, f(·) be their common density. In this paper,the author shows a central limit theorem for a kernel estimate of f(·) under certain regularconditions.     

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.     

行为负相依随机变量阵列加权和的完全收敛性

本文研究了行为NOD随机变量阵列加权和的完全收敛性.运用NOD随机变量列的矩不等式以及截尾的方法,得到了关于行为NOD随机变量阵列加权和的完全收敛性的充分条件.利用获得的充分条件,推广了Baek(2008)关于行为NA随机变量阵列加权和的完全收敛性的结论,得到了比吴群英(2012)更为一般的结果.     

An asymptotic representation for products of random matrices

Let {X_k} be a stationary ergodic sequence of nonnegative matrices. It is shown in this paper that, under mild additional conditions, the logarithm of the i, jth element of X_t···X₁ is well approximated by a sum of t random variables from a stationary ergodic sequence. This representation is very useful for the study of limit behaviour of products of random matrices. An iterated logarithm result and an estimation result of use in the theory of demographic population projections are derived as corollaries.     

An Asymptotic Behavior of the Remainder in the Central Limit Theorem for Moments of Sums of Independent Random Variables

The objective of the paper is to study the asymptotic behavior of the reminder in the central limit theorem for moments of sums of independent random variables.     

Central Limit Theorems for Asymptotically Negatively Associated Random Fields

Abstract The aim of this paper is to investigate the central limit theorems for asymptotically negatively dependent random fields under lower moment conditions or the Lindeberg condition. Results obtained improve a central limit theorem of Roussas [11] for negatively assiated fields and the main results of Su and Chi [18], and also include a central limit of theorem for weakly negatively associated random variables similar to that of Burton et al. [20]. Research supported by National Natural Science Foundation of China (No. 19701011)     

Almost Sure Local Limit Theorems with Rate

We establish almost sure versions, with rate, of the local limit theorem for lattice distributed random variables. We also prove a new delicate correlation inequality for sums of i.i.d. lattice distributed random variables.