关于AQSI序列的几乎处处收敛性及强大数定理

建立了关于AQSI序列的Kolmogorov型不等式和AQSI序列的三级数定理,讨论了AQSI序列的几乎处处收敛性并且得到了关于AQSI序列的chung型强大数定理.     

Laws of Large Numbers for Cesàro alpha-integrable Random Variables under Dependence Condition AANA or AQSI

Both residual Cesàro alpha-integrability (RCI( α)) and strongly residual Cesàro alpha-integrability (SRCI(α)) are two special kinds of extensions to uniform integrability, and both asymp-totically almost negative association (AANA) and asymptotically quadrant sub-independence (AQSI) are two special kinds of dependence structures. By relating the RCI(α) property as well as the SRCI(α) property with dependence condition AANA or AQSI, we formulate some tail-integrability conditions under which for appropriate α the RCI(α) property yields L1-convergence results and the SRCI(α) property yields strong laws of large numbers, which is the continuation of the corresponding literature.     

任意随机序列关于二重非齐次马氏链的随机和的一类随机选择系统的随机逼近定理

本文引进任意随机变量序列随机极限对数似然比的概念,通过测度$\pr$下任意相依随机序列联合分布与测度$\qr$下二重非齐次马氏分布相比较,利用母函数与尾概率母函数工具研究任意受控随机序列之随机和在随机选择系统中的一类随机逼近定理.     

关于任意随机序列的强收敛性

该文的目的是要研究任意随机序列的强极限定理。作为推论，得到了一类鞅差序列的强大数定律，一类随机序列公平比的强极限定理，以及任意随机序列部分和估计定理。      

相依随机序列加权和滑动平均的若干小偏差定理

引入随机序列滑动似然比作为任意二值随机序列相对于Bernoulli分布的独立随机变量序列偏差的一种随机性度量,通过滑动相对熵给出了样本空间的一个子集.在此子集上得到了一类关于随机序列滑动平均的用不等式表示的强极限定理,即小偏差定理,推广了文献[5],[6]等关于随机序列算术平均的结果,这些结果蕴含近期诸多文献的主要结果.     

离散随机序列加权和的若干极限定理

设{Xn,n≥0}是任意离散随机变量序列,{ank,0≤k≤n,n≥0)是一常数阵列,我们引入随机序列渐近对数似然比的概念,作为表征随机序列的真实概率测度P与参考测度Q之间的差异的度量,用分析方法,得到了随机序列Jamison型加权和的若干随机偏差定理.     

独立随机序列最大值的几乎处处极限定理

本文研究了独立随机序列最大值分布的几乎必然收敛性.利用有关协方差的不等式和加权平均,获得独立随机序列最大值的几乎处处极限.将独立同分布随机序列的结论,推广了独立但不同分布的情形.     

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.     

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.     

Chaitin复杂度、事件的Shannon信息量和无穷随机序列(Ⅱ)

本文是文献[1]的继续,主要考虑以下三个方面的问题,第一,从程序复杂度的观点出发,研究Martin-lf(M.L.)无穷随机序列的性质。我们证明了M.L.无穷随机序列是正规的且服从叠对数定律。第二,M.L.无穷随机序列的有效生成。我们证明了利用标准的方法(如投硬币)可以产生可计算概率分布的M.L.无穷随机序列。第三,对非可计算的概率分布,如何定义它的无穷随机序列。文中给出了两种推广定义,并对其中之一进行了较详细的讨论。     

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??Examining the conditions of positively or negatively associated sequences of random variables obeying the strong law of large numbers provided by Alexander, the sequences of Gaussian random variables, nonnegative and uniformly bounded sequences of random variables with general dependent structure were studied, and the sufficient conditions for they obeying the strong law of large numbers were given. At last, an example for Gaussian sequence satisfying the strong law of large numbers was given.     

φ^~混合随机变量列的几乎处处收敛性

本文研究(~φ)混合随机变量列的几乎处处收敛性,获得了(~φ)混合随机变量列的强大数律,推广和改进了独立情形的相应结果.     

über die Definition von effektiven Zufallstests

Summary We continue the discussion on the definition of random sequences from Part I. We will show that the idea of Kolmogoroff to characterize random sequences by their program complexity can be formulated in such a way as to let this definition coÏncide with the others given in Part I. Another equivalent definition of random sequences can be derived from the games of chance. A sequence is random, if and only if no player who calculates his pool by effective methods can raise his fortune indefinitely when playing on this sequence. Finally we will study transformations which preserve the random property of a sequence. We will prove that the original concept of v. Mises can also be modified in such a manner as to coÏncide with all our other definitions. A sequence is random, if and only if it satisfies the strong law of large numbers and if every sequence obtained from it by a constructive measure-preserving transformation is random, too.

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Die Arbeit stellt einen Teil der Habilitationsschrift dar, die der Mathematisch-Naturwissenschaftlichen FakultÄt der UniversitÄt des Saarlandes vom Verfasser vorgelegt wurde.     

两两NQD序列线性形式的强稳定性

本文研究了两两NQD序列线性形式的强稳定性, 得到了不同分布两两NQD序列具有线性形式强稳定性的充分条件.     

Moderate deviations for stationary sequences of Hilbert-valued bounded random variables

In this paper, we derive the Moderate Deviation Principle for stationary sequences of bounded random variables with values in a Hilbert space. The conditions obtained are expressed in terms of martingale-type conditions. The main tools are martingale approximations and a new Hoeffding inequality for non-adapted sequences of Hilbert-valued random variables. Applications to Cramér-Von Mises statistics, functions of linear processes and stable Markov chains are given.     

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.     

On the strong law of large numbers for sequences stationary in the narrow sense

In his recent paper published in Vestnik St. Petersburg University, Ser. Mathematics, V.V. Petrov found new sufficient conditions for the fulfillment of the strong law of large numbers for sequences of random variables stationary in the broad sense. These conditions are expressed in terms of second moments. In this paper, by using the ergodic theorem, similar problems are solved for sequences of random variables stationary in the narrow sense. In the absence of second moments, the statements of conditions involve the truncated second moments of truncated random variables. At the end of the paper, an example of a stationary sequence of random variables which is not ergodic but obeys the strong law of large numbers is given.     

Convergence en loi et lois du logarithme itéré pour les vecteurs gaussiens

In this paper we prove a Strassen version of the law of the iterated logarithm for some sequences of weakly asymptotically independant Banach space valued gaussian random variables which converge in distribution, and we prove that the central limit theorem implies the functional form of the law of the iterated logarithm for the partial sums of certain Banach space valued gaussian sequences.Furthermore we give conditions for the convergence in distribution of sequences of gaussian random variables and gaussian stochastic processes, and these conditions permit us to prove that our results generalize in the gaussian case all similar results known to the authors at present.