强混合序列部分和的强逼近

具有φ（r)一致可积的混合序列的强逼近问题(其中φ（x)/x^2 1↑∞,δ>0)是本所要论述的主题．章给出的结论弥补了[1]中强混合序列的强逼近与独立序列之间的空隙,同时推广了[1]中的结论．     

任意随机序列级数的强收敛性

利用鞅差序列级数收敛定理研究任意随机序列级数的强收敛性,得到了该序列的一个强极限定理,某些经典的鞅差序列和独立随机变量序列的强极限定理是其特例.     

任意随机变量序列的一个强极限定理

研究任意随机变量序列的强收敛性.利用鞅差序列级数收敛定理,证明了任意随机序列的一个强极限定理,作为推论,得到了马氏过程、鞅差序列及独立随机变量序列的强大数定律.     

α-混合序列部分和乘积精确渐近性的一般形式

利用前人获得的α-混合序列部分和乘积的渐近分布的结果,对一般的边界函数和拟权函数得到了α-混合序列部分和乘积的精确渐近性的一般形式.     

任意随机变量序列级数的强收敛性

利用鞅差序列级数的收敛定理和条件三级数定理研究了任意随机变量序列级数的强收敛性,推广了某些经典的鞅差序列和独立随机变量序列及两两NQD序列的强极限定理.     

功率和的强逼近

设$\{\xi_n, n\geq 1\}$是正的随机变量序列, $\ep \xi_1=\theta>0$, 设$S_n = \sum\limits_{i=1}^n \xi_i, Y_n=n\theta\log (S_n/(n\theta))$. 在该文中, 当$\{\xi_n\}$是独立同分布或强平稳$\varphi$ -混合的正随机变量序列时,作者给出功率和$\{Y_n\}$用Wiener过程的强逼近结果.     

关于任意随机变量序列的一类强极限定理

本文的目的是建立一类任意随机变量序列的强极限定理，作为推论，得到了一类鞅差序列收敛定理，马氏过程的强极限定理和若干经典的独立随机变量序列的强大数定律，已有的若干轶差序列收敛定理和可列非齐次马氏链的一个强极限定理是本文结果的特例，本文的主要结果对随机变量序列除矩条件外没有任何要求．     

混合序列加权和的强收敛性

本文给出混合序列加权和的强收敛性的一些充分条件，这些结论推广和改进了文［１］定理３，文［２］定理３；文［３］定理４．１５以及文［４］定理４．     

线性过程的强逼近

该文对由独立同分布随机变量序列所生成的线性过程建立了泛函重对数律和用Wiener过程对线性过程的强逼近结果.     

On Normal Approximation for Strongly Mixing Random Variables

In this paper, we estimate the difference , where Z _n is the sum of n centered and normalized random variables (without the stationarity assumption) satisfying the strong mixing condition, N is a standard normal random variable, and h:ℝ→ℝ is a Lipschitz function. In particular cases, the obtained upper bounds are of order O(n ^−1/2). The research was partially supported by the Lithuanian State Science and Studies Foundation, grant No. T-10/06.     

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 ^(r) _n = 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 ⁽¹⁾ _n +⋯+X ^(r) _n ). 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. Received March 22, 1999, Revised November 6, 2000, Accepted March 16, 2001     

A Strong Law for Mixing Random Variables

A sharp almost sure bound is derived for limit points of average sum of weakly dependent random variables, which ensures strong laws of large numbers for and -mixing random variables, without assumptions on rate of tending to zero of and -mixing parameters _n and _n.     

关于两两NQD随机变量部分和强大数律的注记

In this paper, the authors study the strong law of large numbers for partial sums of pairwise negatively quadrant dependent （NQD） random variables. The results obtained improve the corresponding theorems of Hu et al. （2013）, and Qiu and Yang （2006） under some weaker conditions.     

可交换随机变量序列部分和的完全收敛性

本文通过讨论可交换随机变量序列{Xn：n≥1}的部分和Sn关于正值单调函数H(x)，φ(x)的尾概率级数的收敛性和某种形式矩的存在性之间的关系，获得了部分和完全收敛性的一系列充分性和等价性结论．D     

Strong Limit Theorems for Increments of Sums of Independent Random Variables

We derive universal strong laws for increments of sums of independent, nonidentically distributed, random variables. These results generalize universal results of the author for the i.i.d. case which include the strong law of large numbers, law of the iterated logarithm, Erdos-Renyi law, and Csorgo-Revesz laws. Bibliography: 27 titles. __________ Translated from Zapiski Nauchnykh Seminarov POMI, Vol. 311, 2004, pp. 260–285.     

负相关随机变量加权和的强定律(英文)

在本文中我们讨论了不同分布负相关随机变量加权和的强定律.在一个有限矩生成函数的条件下,一些有关负相关随机变量加权和的强定律被获得.这些结果推广了Soo HakSung[4]关于独立同分布随机变量的相应结论.我们的结果也概括了Mi Hwa Ko和Tae SungKim[7]获得的相关结论,同时使得Nili Sani H R和Bozorgnia A[9]所取得的结果更加形象.     

On the Strong Laws for Weighted Sums of m-negatively Asso ciated Random Variables

In this article, the author establishes the strong laws for linear statistics that are weighted sums of a m-negatively associated（m-NA） random sample. The obtained results extend and improve the result of Qiu and Yang in [1] to m-NA random variables.     

Strong Limit Theorems for Weighted Sums of Negatively Associated Random Variables

In this paper, we establish strong laws for weighted sums of identically distributed negatively associated random variables. Marcinkiewicz-Zygmund’s strong law of large numbers is extended to weighted sums of negatively associated random variables. Furthermore, we investigate various limit properties of Cesàro’s and Riesz’s sums of negatively associated random variables. Some of the results in the i.i.d. setting, such as those in Jajte (Ann. Probab. 31(1), 409–412, 2003), Bai and Cheng (Stat. Probab. Lett. 46, 105–112, 2000), Li et al. (J. Theor. Probab. 8, 49–76, 1995) and Gut (Probab. Theory Relat. Fields 97, 169–178, 1993) are also improved and extended to the negatively associated setting.