Inequalities of Maximum of Partial Sums and Convergence Rates in the Strong Laws for ρ^-mixing Random Variables

在这份报纸，我们建立部分和的 Rosenthal 类型不平等为 -- 混合随机的变量。作为它的应用，我们在强壮的法律得到完全的集中率为 -- 混合随机的变量。获得的结果扩大相应结果。

Some Convergence Results for Sequences of -mixing Random Variables

In this paper, we will present some strong convergence results for sequences of ψ-mixing random variables. The results for sequences of ψ-mixing random variables generalize the corresponding results for independent random variable sequences without any extra conditions.

Strong Law of Large Numbers and Complete Convergence for Sequences of -Mixing Random Variables

We give some theorems of strong law of large numbers and complete convergence for sequences of φ-mixing random variables. In particular, Wittmann＇s strong law of large numbers and Teicher＇s strong law of large nnumbers for independent random variables are generalized to the case of φ -minxing random variables.

Complete Convergence for Arrays of Rowwise Ч-mixing Random Variables

In the paper, the complete convergence for arrays of rowwise φ-mixing random variables is studied. Some sufficient conditions for complete convergence for an array of rowwise φ-mixing random variables without assumptions of identical distribution and stochastic domination are presented.

Moments of the maximum of normed partial sums of ρ^--mixing random variables

Let {Xn,n ≥ 1} be a sequence of identically distributed ρ^--mixing random variables and set Sn =∑i^n=1 Xi,n ≥ 1,the suffcient and necessary conditions for the existence of moments of supn≥1 ｜Sn/n^1/r｜^p（0 〈 r 〈 2,p 〉 0） are given,which are the same as that in the independent case.

Complete Convergence and Weak Law of Large Numbers for <i><span style="text-decoration:overline;">ρ</span></i>-Mixing Sequences of Random Variables

In this paper, the complete convergence and weak law of large numbers are established for ρ-mixing sequences of random variables. Our results extend and improve the Baum and Katz complete convergence theorem and the classical weak law of large numbers, etc. from independent sequences of random variables to ρ-mixing sequences of random variables without necessarily adding any extra conditions.

PRECISE RATE IN THE LAW OF ITERATED LOGARITHM FOR ρ-MIXING SEQUENCE

Let {X,X n;n≥1} be a strictly stationary sequence of ρ-mixing random variables with mean zero and finite variance. Set S n=n k=1X k,M n=max k≤n｜S k｜,n≥1. Suppose lim n→∞ES2 n/n=∶σ2>0 and ∞n=1ρ 2/d(2n)<∞, where d=2,if -1<b<0 and d>2(b+1),if b≥0. It is proved that,for any b>-1, limε0ε 2(b+1)∞n=1(loglogn)bnlognP{M n≥εσ2nloglogn}= 2(b+1)πГ(b+3/2)∞k=0(-1)k(2k+1) 2b+2,where Г(·) is a Gamma function.

Strong Stability of Linear Forms in φ-Mixing Random Variables

In this paper some new results of strong stability of linear forms in φ-mixing random variables are given. It is mainly proved that for a sequence of φ-mixing random variables {xn,n≥1} and two sequences of positive numbers {an,n≥1} and {bn,n≥1} there exist d dn∈R,n = 1,2,..., such that bn^-1∑i=1^naixi-dn→0 a.s.under some suitable conditions. The results extend and improve the corresponding theorems for independent identically distributed random variables.

Laws of the Iterated Logarithm for-mixing Random Variables with Normal Distribution

Consider a ρ-mixing sequence of identically distributed random variables with the underlying dis- tribution in the domain of attraction of the normal distribution. This paper proves that law of the iterated logarithm holds for ρ-mixing sequences of random variables. Our results generalize and improve Theorems 1.2-1.3 of Qi and Cheng （1996） from the i.i.d, case to ρ-mixing sequences.

Complete Convergence for (α,β)-Mixing Random Variables Sequences

The convergence properties are studied through the analysis of （α,β）-mixing random variables sequences in different situations. By using the corresponding inequality, a convergence theorem was presented, and some results for （α,β）-mixing random variables sequences with different distributions were obtained. The results extend the corresponding theorems of independent random variable sequences.

随机-区间混合不确定性多输出模型确认指标

为解决随机和区间变量共存条件下的多输出模型确认问题,提出了一种新的模型确认指标.首先,依据概率方法和区间理论,分析了随机输入变量在实现值条件下随机-区间混合不确定性多输出模型的特点;然后,将基于马氏距离的随机不确定性多输出模型确认方法,推广到随机-区间混合不确定性因素影响下的多输出模型确认之中,定义了一种新的多输出模型确认指标.该指标运用模型输出响应量与试验输出响应量的上、下界马氏距离分布函数曲线之间的面积差异,度量随机-区间混合不确定性条件下多输出模型预测结果与试验结果之间的不一致性.最后,讨论了所提指标的数学性质,给出了指标的计算方法和步骤,通过一个数值算例和一个工程算例验证了指标的正确性与有效性.研究结果表明,当样本数据量充足时,新的模型确认指标能够有效地度量模型输出响应量与试验结果之间差异程度,正确地判断不同多输出模型的优劣,适合于随机-区间混合不确定性因素影响下的多输出模型确认问题.

■-混合序列的弱收敛性和完全收敛性(英文)

在本文中,作者研究了■-混合序列的极限性质。得到了经典的弱收敛定理和完全收敛定理,将独立情形下的弱大数定律、Baum和Katz完全收敛性定理推广到了■-混合序列,而未额外添加任何条件。

行ρ^-混合阵列加权和最大值的完全收敛性

先利用ρ-混合序列Rosenthal型最大值不等式,得到一个关于行ρ-混合阵列加权和最大值的完全收敛性定理,再利用此定理证明ρ-混合序列加权和最大值的MarcinkiewiczZygmund型强大数定律.

ρ^-混合随机变量序列加权和的完全收敛性质

应用ρ-混合随机变量序列截断法、Hlder不等式、Markov不等式、Jensen不等式、Cr不等式及ρ-混合随机变量的Rosenthal型矩不等式,考察在没有同分布假设条件下,ρ-混合随机变量序列加权和的完全收敛性质,并利用Borel-Cantelli引理,给出ρ-混合随机变量序列加权和的Marcinkiewicz-Zygmund型强大数定律.

φ-混合随机变量序列线性形式的强稳定性

利用随机变量的截尾术及强大数定律,得到了φ-混合随机变量序列具有线性形式强稳定性的充分条件.

行-混合随机变量阵列加权和的完全收敛性（英文）

本文研究随机变量阵列加权和的完全收敛性问题,我们获得行-混合随机变量阵列加权和的一个完全收敛性定理.通过这个定理可以获得一系列结果.我们所得结果推广了Baum和Katz(1965),Peligrad和Gut(1999)所得的结果.

ρ^--混合序列的矩不等式及其应用

利用q阶M-Z型序列的一个不等式,获得ρ-混合序列的Rosenthal不等式,利用该不等式,讨论了ρ-混合序列的强大数律,三级数定理,完全收敛性等性质.

混合随机变量的性质及应用

从概率论的基本概念出发引出了混合随机变量的定义,在此基础上给出了常见混合随机变量的几个性质,并将其应用到了两个实际问题中,分析了实际问题中混合随机变量的分布特征.

ψ-混合随机变量线性形式的强稳定性

给出了一些ψ-混合随机变量线性形式的新结果,它推广了独立随机变量的相应定理。

α-混合随机变量序列线性形式的强稳定性

研究了α-混合随机变量序列线性形式的强稳定性.利用随机变量的截尾术及强大数定律,得到了不同分布α-混合随机变量序列具有线性形式强稳定性的充分条件,在某种程度上推广了独立随机变量的线性形式的强稳定性的相应结果.