多维随机向量序列加权和的中心极限定理

本文研究了多维随机向量序列加权和的渐近行为.利用Lindeberg中心极限定理的基本思想,得到了多维随机向量序列加权和的中心极限定理及其收敛速度,为Lindeberg中心极限定理的推广.     

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本文在{ξi}为强混合样本,{ani}是实三角阵列下,得到了一个新的关于线性和n∑i=1aniξi的中心极限定理.并利用该中心极限定理,进一步建立了线性过程部分和的中心极限定理.     

Z~2上相依渗流开簇的极限定理(英文)

中心极限定理, 大偏差定理和大数定律等极限定理在概率论中起着很重要的角色. 本文我们研究Z2上一类相依渗流模型. 对此模型, 我们不仅证明了其无穷开簇的存在唯一性, 而且得到了关于格点盒子类极大开簇的中心极限定理.     

相依序列加权和的几乎处处中心极限定理

该文讨论了非平稳负(正)相依序列加权和的几乎处处中心极限定理,改进并推广了相依序列几乎处处中心极限定理的相关结果.     

绕积马氏链的中心极限定理

讨论了具有离散参数的绕积马氏链的中心极限定理,给出了加在过程样本函数上充分条件。得到了绕积马氏链的中心极限定理成立的充分条件.     

反馈法应用的条件

反馈法应用的条件ＰｅｔｅｒＷ．Ｇｌｙｎｎ．ＤｏｎａｌｄＬ．Ｉｇｌｅｈａｒｔ著王伟译估计静态参数的反馈法是模拟输出分析的基本方法之一。这个方法取决于反馈过程的中心极限定理和出现在中心极限定理中的方差常数的弱相容估计。对于中心极限定理和相容估计给定了一个...     

次线性期望下的一般中心极限定理

受Peng-中心极限定理的启发,本文主要应用G-正态分布的概念,放宽Peng-中心极限定理的条件,在次线性期望下得到形式更为一般的中心极限定理.首先,将均值条件E[X_n]=ε[X_n]=0放宽为|E[X_n]|+|ε[X_n]|=O(1/n);其次,应用随机变量截断的方法,放宽随机变量的2阶矩与2+δ阶矩条件;最后,将该定理的Peng-独立性条件进行放宽,得到卷积独立随机变量的中心极限定理.     

一类马尔可夫骨架过程的强大数定律和中心极限定理

基于马尔可夫骨架过程极限分布的已有研究结果,本文运用波莱尔-康特立引理、更新理论、科尔莫哥洛夫的强大数定律以及独立同分布情形的中心极限定理等重要理论,分别给出了一类马尔可夫骨架过程对应的累积过程满足强大数定律和中心极限定理的充分条件.     

非齐次马氏链的中心极限定理

本文将针对非齐次马氏链的转移矩阵列在Ces`aro收敛意义下,利用鞅的中心极限定理证明一个不同于Dobrushin结果的非齐次马氏链的中心极限定理。     

正常返Markov链的几乎处处中心极限定理

本文考虑不可约正常返Markov链,证明其函数的部分和的几乎处处中心极限定理,把独立同分布(independent and identically distributed,i.i.d.)随机变量序列的几乎处处中心极限定理推广到Markov链,且扩大了使定理成立的权重.     

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.     

A Conditional CLT which Fails for Ergodic Components

We show that the conditional central limit theorem can take place for a stationary process defined on a nonergodic dynamical system while this last does not satisfy the central limit theorem for any ergodic component. There exists an ergodic Markov chain such that the conditional central limit theorem is satisfied for an invariant measure but fails to hold for almost all starting points.      

A note on the central limit theorem for bipower variation of general functions

In this paper we present a central limit theorem for general functions of the increments of Brownian semimartingales. This provides a natural extension of the results derived in [O.E. Barndorff-Nielsen, S.E. Graversen, J. Jacod, M. Podolskij, N. Shephard, A central limit theorem for realised power and bipower variations of continuous semimartingales, in: From Stochastic Analysis to Mathematical Finance, Festschrift for Albert Shiryaev, Springer, 2006], where the central limit theorem was shown for even functions. We prove an infeasible central limit theorem for general functions and state some assumptions under which a feasible version of our results can be obtained. Finally, we present some examples from the literature to which our theory can be applied.     

Parametric estimation for a simple branching diffusion process

A simple branching diffusion process is given as an elementary model of spatial evolution. A parametric estimation theory is presented for this model. As side results, a spatial central limit theorem and spatial strong law of large numbers are also obtained.     

A universal result in almost sure central limit theory

The discovery of the almost sure central limit theorem (Brosamler, Math. Proc. Cambridge Philos. Soc. 104 (1988) 561–574; Schatte, Math. Nachr. 137 (1988) 249–256) revealed a new phenomenon in classical central limit theory and has led to an extensive literature in the past decade. In particular, a.s. central limit theorems and various related ‘logarithmic’ limit theorems have been obtained for several classes of independent and dependent random variables. In this paper we extend this theory and show that not only the central limit theorem, but every weak limit theorem for independent random variables, subject to minor technical conditions, has an analogous almost sure version. For many classical limit theorems this involves logarithmic averaging, as in the case of the CLT, but we need radically different averaging processes for ‘more sensitive’ limit theorems. Several examples of such a.s. limit theorems are discussed.     

A general central limit theorem for strong mixing sequences

A central limit theorem for strong mixing sequences is given that applies to both non-stationary sequences and triangular array settings. The result improves on an earlier central limit theorem for this type of dependence given by Politis, Romano and Wolf in 1997.     

The characteristic polynomial of a random permutation matrix

We establish a central limit theorem for the logarithm of the characteristic polynomial of a random permutation matrix. We relate this result to a central limit theorem of Wieand for the counting function for the eigenvalues lying in some interval on the unit circle.