一类马尔可夫序列最大值的极限律

主要研究一类马尔可夫序列{Xn,n≥0}的最大值的极限分布.导出了这类序列最大值和最小值的分布表达式,利用经典极值理论,建立了规范化最大值max{X0,X1,…,Xn}与i.i.d序列{ξn,n≥1}的规范化最大值max{1ξ,2ξ,…,ξn+1}具有相同极限律的条件.     

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设{Xni,un≤i≤vn,n≥1}与{ani,un≤i≤vn,n≥1}分别为一个随机阵列和一个常数阵列.本文首先引入了随机阵列{Xni,un≤i≤vn,n≥1}关于常数阵列{ani,un≤i≤vn,n≥1}剩余h-可积的概念,它是弱于h-可积,Cesaroα-可积等其它相关可积的定义.然后在这一可积的定义和适当的条件下,我们研究了相依随机序列加权和的强收敛性和平均收敛性,推广并改进了相关文献已有结果.     

行为NA的随机变量阵列加权和的Lr收敛性

{Xni,1 i Kn↑∞,n 1}为行为NA的随机变量阵列,{ani,1 i Kn↑∞,n 1}为实数阵列,研究了∑Kni=1aniXni的Lr收敛性.     

B值平稳线性过程的迭对数律及随机指标中心极限定理

设 { εt;t∈Z}是独立同分布的 B值随机元序列 ,aj;j∈ Z是一实数序列 ,并且 ∞j=-∞| aj| <∞ ,定义平稳线性过程 Xt= ∞j=-∞ajεt- j.本文研究 { Xt;t∈ IN }部分和序列的收敛性质和极限定理 ,给出了 { Xt;t∈ IN }满足有界迭对数律、紧迭对数律及随机指标中心极限定理的充分条件     

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.     

关于图值随机元序列的若干强极限定理

设{ξ_i}_(i=1)~∞是一列独立同分布在图Γ=(V(Γ),E(Γ))上取值的随机元,{a_i}_(i=1)~∞是一列固定的正整数.文章给出图值随机元序列的r-阶权函数、r-阶均值集与r-阶中心序等概念,将随机元序列的Fréchet样本均值的概念推广到更一般的情形,并且讨论了其基本性质,获得了关于图值随机元序列的广义强大数定理.     

三角代数上Jordan积为幂等元处的高阶ξ-Lie可导映射

设u=Tri(A,M,B)是三角代数,{φn}n∈N:u→u是一列线性映射.本文利用代数分解的方法,证明了如果对任意U,V∈u且U。V=P为标准幂等元,有φn([U,V]ξ)=Σi+j=n(φi(U)φj(V)-ξφi(V)φj(U))(ξ≠±1),则{φn}n∈N是一个高阶导子,其中φ0=id为恒等映射,UoV=UV+VU为Jordan积,[U,V]ξ=UV-ξVU为ξ-Lie积.     

带跳分数维积分过程幂变差的渐近行为

研究X_t=∫_0~tφ_sdB_s~H+ξ_t现实幂变差渐近行为,B~H为Hurst指数H∈(0,1)分数维Brown运动,φ为具有有限q次变差的随机过程且q1/(1-H),ξ为独立于B~H不含Gauss项的Levy过程,建立现实幂变差幂次为1/H的中心极限定理,得到现实截断幂变差大数定律和中心极限定理.     

ON CHOVER''''S LIL FOR THE WEIGHTED SUMS OF STABLE RANDOM VARIABLES

Let {Xn,n ≥ 1} be a sequence of α-stable random variables(0 < α < 2), {ani,1 ≤ i≤ n, n≥1} be an array of constant real numbers. Under some restriction of {ani,1 ≤ i ≤ n,n≥1}, the authors discuss the integral test for the weighted partial sums {Σi=1naniXi,n ≥ 1}, and obtain the Chover's laws of iterated logarithm(LIL) as corollaries.     

带跳的Gauss积分过程幂变差的渐近行为

研究了Xt=∫t0φsdGs+ξt已实现幂变差的渐近理论,其中G为平稳增量Gauss过程,φ为随机过程,ξ为与G独立的非Gauss Lévy过程,而积分为按路径Riemann-Stietjes积分.给出了经适当规范化后已实现幂变差的概率极限定理以及相应的中心极限定理,这些结果将为处理长期记忆跳过程的统计问题提供理论基础.     

Statistical properties for nonhyperbolic maps with finite range structure

We establish the central limit theorem and non-central limit theorems for maps admitting indifferent periodic points (the so-called intermittent maps). We also give a large class of Darling-Kac sets for intermittent maps admitting infinite invariant measures. The essential issue for the central limit theorem is to clarify the speed of -convergence of iterated Perron-Frobenius operators for multi-dimensional maps which satisfy Renyi's condition but fail to satisfy the uniformly expanding property. Multi-dimensional intermittent maps typically admit such derived systems. There are examples in section 4 to which previous results on the central limit theorem are not applicable, but our extended central limit theorem does apply.

     

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.     

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.      

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.     

GI/G/1排队系统队长的强大数定律和中心极限定理

本文首先证明当服务强度小于1时,GI/G/1排队系统的队长是一个特殊的马尔可夫骨架过程——正常返的Doob骨架过程,然后运用马尔可夫骨架过程的强大数定律和中心极限定理等重要结果,给出了队长的累积过程的期望和方差,并给出了该累积过程满足强大数定律和中心极限定理的充分条件。     

Stein's method and random character ratios

Stein's method is used to prove limit theorems for random character ratios. Tools are developed for four types of structures: finite groups, Gelfand pairs, twisted Gelfand pairs, and association schemes. As one example an error term is obtained for a central limit theorem of Kerov on the spectrum of the Cayley graph of the symmetric group generated by -cycles. Other main examples include an error term for a central limit theorem of Ivanov on character ratios of random projective representations of the symmetric group, and a new central limit theorem for the spectrum of certain random walks on perfect matchings. The results are obtained with very little information: a character formula for a single representation close to the trivial representation and estimates on two step transition probabilities of a random walk. The limit theorems stated in this paper are for normal approximation, but many of the tools developed are applicable for arbitrary distributional approximation.

     

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We establish a quenched central limit theorem (CLT) for the branching Brownian motion with random immigration in dimension $d\geq4$. The limit is a Gaussian random measure, which is the same as the annealed central limit theorem, but the covariance kernel of the limit is different from that in the annealed sense when d=4.     

Power-law estimates for the central limit theorem for convex sets

We investigate the rate of convergence in the central limit theorem for convex sets established in [B. Klartag, A central limit theorem for convex sets, Invent. Math., in press. [8]]. We obtain bounds with a power-law dependence on the dimension. These bounds are asymptotically better than the logarithmic estimates which follow from the original proof of the central limit theorem for convex sets.     

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.     

A central limit theorem for integrals with respect to random measures

Integrals with respect to stationary random measures are considered. A central limit theorem for such integrals is proved. The results are applied to obtain a functional central limit theorem for transformed solutions of the Burgers equation with random initial data.