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 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.

Complete Convergence for Weighted Sums of φ-mixing Sequence

In this paper, the complete convergence and strong law of large numbers for weighted sums of φ-mixing sequence with different distribution are investigated under some weaker moment conditions. Our results extend ones of independent sequence with identical distribution to the case of φ-mixing sequence with different distribution.

equivalent conditions of complete convergence for m-dimensional products of iid random variables and application to strong law of large numbers

Under very weak condition 0 【 r(t)↑∞ , t→∞. we obtain a series of equivalent conditions of complete convergence for maxima of m-dimensional products of iid random variables, which provide a useful tool for researching this class of questions. Some results on strong law of large numbers are given such that our results are much stronger than the corresponding result of Gadidov’s.

EQUIVALENCE OF COMPLETE CONVERGENCEAND LAW OF LARGE NUMBERS FOR B-VALUED RANDOM ELEMENTS

Under some conditions on probability, this note discusses the equivalence between the complete convergence and the law of large number for B-valued independent random elements. The results of [10] become a simple corollary of the results here. At the same time, the author uses them to investigate the equivalence of strong and weak law of large numbers, and there exists an example to show that the conditions on probability are weaker.

Some Convergence Properties for Weighted Sums of Martingale Difference Random Vectors

Let{X_(ni),F_(ni);1≤i≤n,n≥1}be an array of R^(d)martingale difference random vectors and{A_(ni),1≤i≤n,n≥1}be an array of m×d matrices of real numbers.In this paper,the Marcinkiewicz-Zygmund type weak law of large numbers for maximal weighted sums of martingale difference random vectors is obtained with not necessarily finite p-th(1<p<2)moments.Moreover,the complete convergence and strong law of large numbers are established under some mild conditions.An application to multivariate simple linear regression model is also provided.

A Remark on Strong Law of Large Numbers for Weighted U-Statistics

Let （X, Xn; n≥ 1} be a sequence of i.i.d, random variables with values in a measurable space （S,8） such that E｜h（X1, X2,..., Xm）｜ 〈 ∞, where h is a measurable symmetric function from Sm into R = （-∞, ∞）. Let {wn,i1,i2 im ; 1 ≤ i1 〈 i2 〈 …… 〈im 〈 n, n ≥ m} be a matrix array of real numbers. Motivated by a result of Choi and Sung （1987）, in this note we are concerned with establishing a strong law of large numbers for weighted U-statistics with kernel h of degree m. We show that whenever SUP n≥m max1〈i1〈i2〈…〈im≤｜wn i1,i2 i,im｜ 〈∞, where 0 = Eh（X1, X2,..., Xm）. The proof of this result is based on a new general result on complete convergence, which is a fundamental tool, for array of real-valued random variables under some mild conditions.

On the convergence for PNQD sequences with general moment conditions

Let fX;Xn;n≥1g be a sequence of identically distributed pairwise negative quadrant dependent(PNQD)random variables and fan;n1g be a sequence of positive constants with an=f(n)and f(θ^k)=f(θ^k-1)for all large positive integers k,where 1<θ≤βand f(x)>0(x≥1)is a non-decreasing function on[b;+1)for some b≥1:In this paper,we obtain the strong law of large numbers and complete convergence for the sequence fX;Xn;n≥1g,which are equivalent to the general moment conditionΣ∞n=1P(|X|>an)<1.Our results extend and improve the related known works in Baum and Katz[1],Chen at al.[3],and Sung[14].

Complete Moment and Integral Convergence for Sums of Negatively Associated Random Variables

For a sequence of identically distributed negatively associated random variables {Xn; n ≥ 1} with partial sums Sn = ∑i=1^n Xi, n ≥ 1, refinements are presented of the classical Baum-Katz and Lai complete convergence theorems. More specifically, necessary and sufficient moment conditions are provided for complete moment convergence of the form ∑n≥n0 n^r-2-1/pq anE（max1≤k≤n｜Sk｜^1/q-∈bn^1/qp）^＋〈∞to hold where r 〉 1, q 〉 0 and either n0 = 1,0 〈 p 〈 2, an = 1,bn = n or n0 = 3,p = 2, an = 1 （log n） ^1/2q, bn=n log n. These results extend results of Chow and of Li and Spataru from the indepen- dent and identically distributed case to the identically distributed negatively associated setting. The complete moment convergence is also shown to be equivalent to a form of complete integral convergence.

Complete Convergence and Complete Moment Convergence for Maximal Weighted Sums of Extended Negatively Dependent Random Variables

In this paper, the complete convergence and complete moment convergence for maximal weighted sums of extended negatively dependent random variables are investigated. Some sufficient conditions for the convergence are provided. In addition, the Marcinkiewicz Zygmund type strong law of large numbers for weighted sums of extended negatively dependent random variables is obtained. The results obtained in the article extend the corresponding ones for independent random variables and some dependent random variables.

关于多指标变量的Marcinkiewicz大数律和完全收敛性

该文讨论了两两NQD多指标随机变量序列{X-k;k∈Nd}(d≥2)的Marcinkiewicz型弱大数律和强大数律,同时得到了一个关于多指标变量部分和完全收敛的充要条件.

完全收敛性中的Paley不等式

本文研究了完全收敛性中的Paley不等式,得到了Hsu-Robbins-Erds大数定律的更加精确的下界和上界,讨论了文[1]中提出的问题,同时在更加广泛的大数定律的意义下推广了[1]的结果,得到了比文[2,3]更加精确的结论。

NA随机变量加权和的极限结果

该文研究了NA随机变量序列加权和的Marcinkiewcz-Zygmund强大数定律和完全收敛性.这些结果推广和完善了Bai和Cheng[1]和Cuzick[2]的结果.

行ALNQD阵列加权和最大值的完全收敛性

利用渐近线性坐标负相依(ALNQD)序列最大值的矩不等式,得到了行为ALNQD阵列加权和最大值的完全收敛性定理,并利用该定理证明了ALNQD序列加权和最大值的Marcinkiewicz-Zygmund型强大数定律.

B值随机元的Rosenthal不等式及其应用

该文证明了一类B值随机元序列的Rosenthal不等式,一些经典的Rosenthal不等式作为其推论被蕴含其中.作为不等式的应用,还给出了行为鞅差随机元的阵列的完全收敛性的一些结果,推广和改进了一些熟知的结论.

矩条件下NA随机变量的强极限定理

令{X,Xn,n≥1}为同分布的NA随机变量序列,{an,n≥1}是一正常数序列且an/n↑.讨论了{X,Xn,n≥1}的强大数定律和完全收敛性,得到了与条件∞∑n=1P(|X|>an)<∞等价的结果.另外,该结果推广了关于两两独立同分布序列的相应结果.

多指标随机变量Marcinkiewicz大数定律完全收敛性和收敛速度

本文考虑指标在，ｄ≥１中的独立同分布随机变量序列，得到了有关大数定律的完全收敛性和收敛速度等一些结果．

概率论教学偶得

强大数定律揭示了随机现象的偶然性与必然性之间的关系.本文分享概率论教学的一个偶得,以激发学生兴趣.

一般随机变量的完全收敛及大数定律

通过包含并完善一些已有结论,建立了一般随机变量的完全收敛和大数定律.特别对于两两负象限相关的随机变量,得到了其完全收敛和Marcinkiewicz-Zygmund型强大数定律之间的等价结论.

Strong Approximation Theorems for Sums of Random Variables when Extreme Terms are Excluded

Let{X_n:n≥1}be a sequence of i.i.d.random variables and let X_n^((r))=X_j if|X_j| is the r-th maximum of |X_1|……|X_n|.Let S_n=X_1+…+X_n and ^(r)S_n=S_n(X_n^(1)+…+X_n^(r)).Sufficient and necessary conditions for ^(r)S_n approximating to sums of independent normal random variables are obtained.Via approximation results,the convergence rates of the strong law of large numbers for ^(r)S_n are studied.