AANA随机变量序列的中心极限定理

研究了渐近几乎负相依(简称为AANA)随机变量序列的渐近正态问题.在非常一般的条件下,得到了AANA序列的中心极限定理,推广了负相依(简称为NA)、独立随机变量序列的相应结论.     

NA序列中心极限定理的收敛速度

本文对NA（NegativelyAssociated）序列建立了中心极限定理的一致收敛速度，只要其三阶矩有限及描述NA序列协方差结构的一个系数u（n）被负指数序列所控制，而无需平稳性便获得了其收敛速度O（n(-1/2)logn）。     

非平稳NA序列中心极限定理的一些结果

本文讨论了非平稳同分布ＮＡ序列的渐近正态问题，给出了两个中心极限定理，以往的文献在讨论ＮＡ序列的这些问题时，多加有强平稳的限制；但是大量问题所出现的ＮＡ序列却多为非平稳的，因此有开展研究的必要。本文在寻求摆脱平稳性限制的途径方面作了有益的尝试，所得的定理不仅可解决一大类非平稳ＮＡ序列的渐近正态问题，而且将以往的强平稳场合的结果包含为特例，具有一定的理论意义与应用价值。     

α混合随机变量序列部分和乘积的几乎处处中心极限定理

利用子序列等方法,获得α混合随机变量序列部分和乘积的几乎处处中心极限定理的更优结果,改进了相关文献的结果.     

鞅差序列加权和的中心极限定理

对一类鞅差序列,我们获得了其加权和中心极限定理,并给出了在非线性回归模型及线性模型中的应用。     

大数定律与中心极限定理之关系

用格涅坚克等定理及具体例子说明大数定律与中心极限定理之间的关系。     

随机变量序列函数的几乎处处中心极限定理

该文证明了随机元序列的一个一般的几乎处处中心极限定理, 并把这一结论应用于随机变量序列的函数.     

关于关联随机变量序列的极限收敛定理

本文给出了两两PQD的随机变量序列的级数收敛及加权部分和的稳定性的两个结果。另给出了相伴(associated)序列加权和的稳定性的一个结果。最后给出了一个级数收敛定理条件不能减弱的反例。     

一类适应随机变量序列的强极限定理

研究了一类适应随机变量序列的局部收敛性,推广了文献[1]中的结论.并在假定部分和序列为极限鞅时,得到了极限鞅的强极限定理.最后给出了*-mixing序列的强大数定律.     

案例式中心极限定理教学研究

以案例教学法作为中心极限定理教学的主要手段,选取贴近实际生活的案例——合理规划用电问题和竞争问题,作为教学切入点,激发学生学习兴趣,引导学生从实际情景中发现问题解决问题.课堂实践表明,案例式教学加深了学生对中心极限定理的理解和应用,开阔了学生视野,并对促进课堂教学,课程建设,师生能力提高都有重要意义.     

The Stein--Tikhomirov Method and a Nonclassical Central Limit Theorem

We present a simplified version of the Stein--Tikhomirov method realized by defining a certain operator in the class of twice differentiable characteristic functions. Using this method, we establish a criterion for the validity of a nonclassical central limit theorem in terms of characteristic functions.     

Central Limit Theorem for Random Strict Partitions

We consider the set of all partitions of a number n into distinct summands (the so-called strict partitions) with the uniform distribution on it and study fluctuations of a random partition near its limit shape, for large n. The use of geometrical language allows us to state the problem in terms of the limit behavior of random step functions (Young diagrams). A central limit theorem for such functions is proven. Our method essentially uses the notion of large canonical ensemble of partitions. Bibliography: 7 titles.     

Spatial Central Limit Theorem for Supercritical Superprocesses

We consider a measure-valued diffusion (i.e., a superprocess). It is determined by a couple \((L,\psi )\), where L is the infinitesimal generator of a strongly recurrent diffusion in \(\mathbb {R}^{d}\) and \(\psi \) is a branching mechanism assumed to be supercritical. Such processes are known, see for example, (Englander and Winter in Ann Inst Henri Poincaré 42(2):171–185, 2006), to fulfill a law of large numbers for the spatial distribution of the mass. In this paper, we prove the corresponding central limit theorem. The limit and the CLT normalization fall into three qualitatively different classes arising from “competition” of the local growth induced by branching and global smoothing due to the strong recurrence of L. We also prove that the spatial fluctuations are asymptotically independent of the fluctuations of the total mass of the process.     

A Central Limit Theorem for isotropic flows

We establish that the image of a measure, which satisfies a certain energy condition, moving under a standard isotropic Brownian flow will, when properly scaled, have an asymptotically normal distribution under almost every realization of the flow. We derive the same result for an initial point mass moved by an isotropic Kraichnan flow.     

关于两两NQD随机序列的一个极限定理

设为两两NQD随机序列, 且, 是一列严格单调递增的凸序列. 本文将 Feller (1946)关于独立同分布期望不存在随机序列的极限定理推广到两两NQD随机 序列的情形.     

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.     

A Central Limit Theorem for linear random fields

A Central Limit Theorem is proved for linear random fields when sums are taken over union of finitely many disjoint rectangles. The approach does not rely upon the use of Beveridge-Nelson decomposition and the conditions needed are similar in nature to those given by Ibragimov for linear processes. When specializing this result to the case when sums are being taken over rectangles, a complete analogue of the Ibragimov result is obtained for random fields with a lot of uniformity.