迭代Brown运动的一个Chung型重对数律

X及Y分别为Rd1及Rd2中的相互独立的标准Brown运动,满足X（0）=Y（0）=0．定义,称为一个迭代Brown运动．本文给出了关于Zd1,d2的一个Chung型重对数律．     

加权U—统计量的一个重对数律

证明了关于独立同分布随机变量序列的加权Ｕ－统计量的一个重对数律,类似于献「３」证明了一个加权Ｕ－统计量的解耦不等式。     

A Functional Law of the Iterated Logarithm for U-Statistic Type Processes

We prove a functional law of the iterated logarithm for U-statistics type processes. The result is used to determine the almost sure set of limit points for change-point estimators.     

ρ-混合序列的重对数律

设{Xn,n≥1}是同分布ρ-混合序列,其分布属于特征指数为α(0<α<2) 的非退化稳定分布的正则吸引场,证明了依概率1有lira supn→∞ = e1/α,并获得了一系列等价条件．此结果的获得不仅将已有的一些结果推广至ρ-混合序列的情形,并且将其结果作了一定的改进．     

一类随机变量序列加权和的Chover型重对数律

对于具有某种尾渐近行为的独立同分布的随机变量序列,本文通过积分检验刻划了其加权部分和的极限结果,并作为推论获得了Chover型重对数律。把这些结果应用到经典的可和方式,获得了相应的结果。     

Strassen-Type Law of the Iterated Logarithm for Self-normalized Sums

Let {X,X _n ,n≥1} be a sequence of independent identically distributed random variables with EX=0 and assume that EX ² I(|X|≤x) is slowly varying as x→∞. In this paper it is shown that a Strassen-type law of the iterated logarithm holds for self-normalized sums of such random variables, i.e., when X is in the domain of attraction of the normal law.     

Iterated Logarithm Law for Anticipating Stochastic Differential Equations

We prove a functional law of iterated logarithm for the following kind of anticipating stochastic differential equations

Law of the Iterated Logarithm for Unstable Gaussian Autoregressive Models

We investigate the asymptotic properties of one-dimensional Gaussian autoregressive processes of the second order. We prove the law of the iterated logarithm in the case of an unstable autoregressive model.     

非同分布NA列的对数律和重对数律

给出了非同分布NA列满足对数律和重对数律的一些矩条件，而文[50-[7]中的部分结果可以成为其特殊情形并得到加强．     

和的乘积的重对数律

设{X,X_n,n≥1}是独立的或φ -混合的或 ρ -混合的正的平稳随机变量序列，或$\{X,X_n,n≥1}$是正的随机变量序列使得{X_n-EX,n≥1\} 是平稳遍历的鞅差序列，记S_n=\sum\limitsⁿ_{j=1}X_j, n≥1 . 该文在条件EX=μ> 0 及0 Var(X)<∞下，证明了部分和的乘积$\prod\limits^n_{j=1}S_j/n!\mu^n$在合适的正则化因子下的某种重对数律.     

A generalization of the Iterated Logarithm Law for weighted sums with infinite variance

Summary A generalization of the classical Law of the Iterated Logarithm (LIL) is obtained for the weighted i.i.d. case consisting of sequences { _n Y _n } where the weights { _n } are nonzero constants and {Y _n} are i.i.d. random variables. If Y is symmetric but not necessarily square integrable and if the weights satisfy a certain growth rate, conditions are given which guarantee that { _n Y _n} obey a Generalized Law of the Iterated Logarithm (GLIL) in the sense that almost certainly for some positive conslants a _n . Teicher has shown that such weights entail the classical LIL when EY ²< and Feller has treated the GLIL when _n =1 and EY ²=. The main finding here asserts that if {q_n} satisfies q _n ² =nG(q_n)loglogq _n where G is a specified slowly varying function, asymptotically equivalent to the truncated second moment of Y, and if a certain series converges, then the GLIL obtains with where .     

A Law of the Iterated Logarithm for Extreme Queue Length in Multiphase Queues

The object of this research in queueing theory is the Law of the Iterated Logarithm (LIL) under the conditions of heavy traffic in Multiphase Queueing Systems (MQS). In this paper, the LIL is proved for extreme values of important probabilistic characteristics of the MQS investigated as well as maxima and minima of the summary queue length of customers and maxima and minima of the queue length of customers. Also, the paper presents a survey on the works for extreme values in queues and the queues in heavy traffic.      

Rates of Convergence and Law of the Iterated Logarithm for U-Processes

We study in this paper some limit theorems for U-processes. We calculate rates of convergence in the central limit theorem of nondegenerate U-processes under metric entropy with bracketing condition. In application, we improve upon the law of the iterated logarithm of Arcones. All calculations use the Ossiander chaining procedure.     

关于可交换随机变量序列的重对数律

本文讨论了可交换随机变量序列{Xn:n≥1}的重对数律。     

The Law of the Iterated Logarithm for Finitely Inhomogeneous Random Walks

The Hartman–Wintner–Strassen law of the iterated logarithm states that if X ₁, X ₂,… are independent identically distributed random variables and S _n =X ₁+???+X _n , then

$\limsup_{n}S_{n}/\sqrt{2n\log \log n}=1\quad \text{a.s.},\qquad \liminf_{n}S_{n}/\sqrt{2n\log \log n}=-1\quad \text{a.s.}$

if and only if EX ₁ ² =1 and EX ₁=0. We extend this to the case where the X _n are no longer identically distributed, but rather their distributions come from a finite set of distributions.

     

On the Law of The Iterated Logarithm for Rapidly Increasing Subsequences

The usual law of the iterated logarithm states that the partial sums S_n of independent and identically distributed random variables can be normalized by the sequence a_n = √nlog log n, such that limsup_n→∞ S_n/a_n = √2 a.s. As has been pointed out by Gut (1986) the law fails if one considers the limsup along subsequences which increase faster than exponentially. In particular, for very rapidly increasing subsequences {n_k≥1} one has limsup_k→∞ S_nk/a_nk = 0 a.s. In these cases the normalizing constants a_nk have to be replaced by √nk log k to obtain a non-trivial limiting behaviour: limsup_k→∞ S_nk/ √nk log k = √2 a.s. We will present an intelligible argument for this structural change and apply it to related results.     

线性过程的强逼近和重对数律

本文讨论由独立同分布随机变量列产生的线性过程的泛函型重对数律和强逼近, 同时又给出由NA随机变量列产生的线性过程的重对数律.