不同分布(φ)混合序列的最大值不等式及其应用

利用矩不等式和截尾的方法,讨论了不同分布的φ混合序列的最大值不等式.作为应用,获得了混合序列的一阶矩及p（p〉1）阶矩分别存在有限的充分条件,这是一个与独立同分布情形一致的结果.     

ρ~--混合序列部分和随机乘积的渐近分布

ρ~--混合序列是各种相依类型中较弱的一种,研究其极限性质具有一般意义.利用ρ~--混合序列部分和乘积的渐近分布及部分和最大值的矩不等式,得出了ρ~--混合序列部分和随机乘积的渐近分布.     

关于任意离散随机序列的一个强偏差定理

引用极限对数似然比的概念作为任意随机序列联合分布与其边缘分布"不相似性"的度量,构造几乎处处收敛的上鞅,讨论了任意离散随机序列的强偏差定理.     

行为混合阵列加权和的收敛性

本文研究了行为混合阵列加权和的收敛性.利用混合序列的Rosenthal型最大值不等式,讨论了混合阵列加权和的L1收敛性,依概率收敛性,几乎处处收敛性,及完全收敛性之间的等价关系,推广了行独立随机变量阵列相应的结果.     

含趋势项强相依非平稳序列的两个重要分布

{ηn}为平稳标准化正态序列,相关系数r|t-j|=Cov(ηu,ηj),若rnlogn→∞时,Leadbetter[1]等得到了序列最大值的渐近分布.本文考虑非平稳带有趋势项序列{ηn},得到了序列最大值的渐近分布和最大值与部分和的联合渐近分布.     

关于具有随机足标的极值之注记

具有随机足标的极值之弱收敛研究已有所见，所涉及的随机序列多限于ｉ，ｉ，ｄ．序列的情形。本文就最大值序列关联某个确定的分布而原序列不一定是ｉ．ｉ．ｄ。的情形下，对具有随机足标的最大值这极限分布问题，作点注记。     

鞅差序列加权和的几乎处处收敛(英文)

本文研究了一类鞅差序列加权和的收敛性的问题.利用一些基本不等式和截尾技术,获得了加权和的几乎处处收敛性,推广了关于独立同分布的随机变量序列的相关结果.     

高斯序列的最大值的几乎必然极限

本文研究了高斯序列{Xn}最大值的几乎必然极限。利用正态比较引理和对数平均,在有关协方差的某些条件下,得到了最大值的一个几乎必然极限定理.     

关于任意非负随机序列随机和的一类随机偏差定理

本文引入任意随机变量序列随机极限对数似然比概念,作为任意相依随机序列联合分布与其边缘乘积分布“不相似”性的一种度量,利用构造新的密度函数方法来建立几乎处处收敛的上鞅,在适当的条件下,给出了任意受控随机序列的一类随机偏差定理.     

AR(1)序列的样本均值分布的随机加权逼近

随机加权方法是郑忠国近期提出的逼近统计量分布的新方法.目前,已有许多文章讨论了此方法对一些统计量分布的逼近问题.但这些结果仅限于对独立随机样本的研究.本文将此方法应用于相依随机序列——AR(1)序列的样本均值,并得到了和独立情形同样好的结果.     

Almost sure convergence of sums of maxima and minima of positive random variables

For a sequence of independent and identically distributed positive random variables, the almost sure convergence of sums of maxima (when suitably normalized) to appropriate constants is proved for both bounded and unbounded random variables. A similar result is also proved for sums of minima of such variables.     

距离空间上的几乎处处中心极限定理

该文得到了关于一般可分距离空间上独立随机元序列的几乎处处中心极限定理(almost sure central limit theory, 简记为ASCLT). 作为应用, 该文给出了取值于可分Banach空间上随机元序列以及一类随机场序列满足ASCLT的充分条件,最后给出了关于多维随机变量序列极值的ASCLT.     

Almost Sure Central Limit Theorem for Partial Sums of Markov Chain

The authors prove an almost sure central limit theorem for partial sums based on an irreducible and positive recurrent Markov chain using logarithmic means,which realizes the extension of the almost sure central limit theorem for partial sums from an i.i.d.sequence of random variables to a Markov chain.     

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This paper considers the asymptotics of randomly weighted sums and their maxima, where the increments {X_i,i\geq1\} is a sequence of independent, identically distributed and real-valued random variables and the weights {\theta_i,i\geq1\} form another sequence of non-negative and independent random variables, and the two sequences of random variables follow some dependence structures. When the common distribution F of the increments belongs to dominant variation class, we obtain some weakly asymptotic estimations for the tail probability of randomly weighted sums and their maxima. In particular, when the F belongs to consistent variation class, some asymptotic formulas is presented. Finally, these results are applied to the asymptotic estimation for the ruin probability.     

Almost sure limit points of independent copies of sample maxima

In this paper, we derive the almost sure limit sets of the random vector consisting of properly normalised independent copies of sample maximum of an i.i.d. sequence. The sets are derived under two different conditions on the common distribution function.     

Almost sure limit theorems of mantissa type for semistable domains of attraction

A certain class of stochastic summability methods of mantissa type is introduced and its connection to almost sure limit theorems is discussed. The summability methods serve as suitable weights in almost sure limit theory, covering all relevant known examples for, e.g., normalized sums or maxima of i.i.d. random variables. In the context of semistable domains of attraction the methods lead to previously unknown versions of semistable almost sure limit theorems. This research has been carried out while the author was staying at the University of Debrecen, Hungary, with the kind support of Deutsche Forschungsgemeinschaft.     

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.     

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.     

An almost sure limit theorem for random sums of independent random variables in the domain of attraction of a semistable law

In this paper we prove an almost sure limit theorem for random sums of independent random variables in the domain of attraction of a p-semistable law and describe the limit law.