均值无限的随机游动上确界的尾渐近性和局部渐近性

对于增量具有无限均值及长尾分布的随机游动, Denisov D.等给出了其上确界的尾渐近性的一个充分条件.本文将增量的长尾分布的范围扩大到一个更大的分布族,它真包含了长尾分布族和控制变化尾分布族等.同时,证明了上述的充分条件也足必要的.为此,研究了这个更大分布族的性质,给出了积分加权分布是长尾或次指数的一些充分条件.相应地,还得到了增量具有无限均值的随机游动上确界的局部渐近性的一个等价条件.     

n阶本原双随机矩阵遍历指数的上确界

本文给出了ｎ阶本原双随机矩阵遍历指数的上确界：     

含参数随机环境中(2,1)随机游动M估计量的渐近正态性

考虑随机环境中(2,1)随机游动,其中环境独立同分布,包含特定参数.[Stoch.Models,2019,35(3):338-356]基于对游动轨道的一次观测,对环境参数构造了M-估计量,并得到其弱相合性.本文进一步证明M-估计量的渐近正态性.研究工具是构造(2,1)随机游动的内蕴分支结构.虽然M-估计量没有解析表达式,但可借助内蕴分支结构,通过建立准则函数梯度向量的中心极限定理,得到M-估计量的渐近正态性,其极限协方差矩阵正是Fisher信息矩阵的逆矩阵.本文还给出了Fisher信息矩阵的估计方法.最后进行数值模拟,验证了所提出的估计量的弱相合性;根据理论得到的渐近正态分布结论,进一步构造经验置信区间,验证了结果的合理有效性.     

迹非零的布尔矩阵的幂敛指数的上确界

设是恰含ｄ个正对角元的ｎ阶布尔矩阵的集合，１≤ｄ≤ｎ．本文在柳柏濂、邵嘉裕１９９１年工作的基础上进一步证明了同时证明：这个界是最好可能的。从而，完全解决了的最大幂敛指数问题。     

直线上时间随机环境下随机游动的渐近性质

在状态空间是可数情形下,本文给出了时间随机环境下随机游动的一个一般模型.随后,在环境是独立同分布情形下得到了直线上时间随机环境下紧邻随机游动的一个常返与暂留准则和强大数定律;最后讨论了其中心极限定理,它类似与简单随机游动的相应结果.     

序有界模糊n-方体数集的上确界与下确界

本文中,我们讨论了在定义模糊n-方体数值映射的某种类型的积分时要用到的序有界模糊n-方体数集的上确界与下确界的问题。我们证明了序有界模糊n-方体数集的上、下确界的存在性,并给出了它们的表示。     

直线上独立时间随机环境中随机游动的渐近行为

主要讨论直线上独立时间随机环境中随机游动的常返性和非常返性,以及该过程的中心极限定理.     

半直线上随机环境中的随机游动的常返性

本文讨论半直线上随机环境中的随机游动的常返性。在独立环境下，主要通过强大数定律，找到了非常返和正常返的一个充分条件下，并将这一结果推广到一些特殊情情形。在一般的随机环境下，主要通过数列的有界性，给出了常返与零常返的一个充分条件。     

一类随机环境下随机游动的常返性

就一类平稳环境θ下随机流动{Xn}n∈z 建立相应的Markov-双链{ηn}n∈z ={(xn,Tnθ)}n∈z ,并给出在该平稳环境θ下{xn}n∈z 为常返链的条件.     

Asymptotics for Random Walks with Dependent Heavy-Tailed Increments

We consider a random walk {S _n} with dependent heavy-tailed increments and negative drift. We study the asymptotics for the tail probability P{sup _n S _n >x} as x. If the increments of {S _n} are independent then the exact asymptotic behavior of P{sup _n S _n >x} is well known. We investigate the case in which the increments are given as a one-sided asymptotically stationary linear process. The tail behavior of sup _n S _n turns out to depend heavily on the coefficients of this linear process.     

Asymptotics of the Moment Generating Function for the Range of Random Walks

The range of random walks means the number of distinct sites visited at least once by the random walk before time n. We are interested in the free energy function of the range of simple symmetric random walks and determine the asymptotic behavior near the origin.     

Asymptotics for Sums of Random Variables with Local Subexponential Behaviour

We study distributions F on [0,) such that for some T , F ^*2(x, x+T] 2F(x, x+T]. The case T = corresponds to F being subexponential, and our analysis shows that the properties for T < are, in fact, very similar to this classical case. A parallel theory is developed in the presence of densities. Applications are given to random walks, the key renewal theorem, compound Poisson process and Bellman–Harris branching processes.     

Estimates for the Distributions of the Sums of Subexponential Random Variables

Let be a random walk with independent identically distributed increments . We study the ratios of the probabilities P(S _n >x) / P(₁ > x) for all n and x. For some subclasses of subexponential distributions we find upper estimates uniform in x for the ratios which improve the available estimates for the whole class of subexponential distributions. We give some conditions sufficient for the asymptotic equivalence P(S > x) E P(₁ > x) as x . Here is a positive integer-valued random variable independent of . The estimates obtained are also used to find the asymptotics of the tail distribution of the maximum of a random walk modulated by a regenerative process.     

随机游动的阶梯高度和最大值及其在风险理论中的应用

该文研究了均值为负的实值随机游动的阶梯高度及最大值, 在指数估计的条件不满足的情况下,得到了它们分布的局部渐近估计和尾渐近估计, 并将这些结果应用到风险理论中的Sparre Andersen 风险模型上, 得到了一些关于破产概率的新结果.     

一类随机徘徊的等待时间

以一种随机徘徊为例,说明由独立增量点过程的等待时间生成的两类随机量——点间间距、给定时刻前后两次事件出现的时间差——之间的关系.     

Strong Laws of Large Numbers for Random Walks in Random Sceneries

In this paper, we study strong laws of large numbers for random walks in random sceneries. Some mild sufficient conditions for the validity of strong laws of large numbers are obtained.     

Tail Asymptotics for the Supremum of a Random Walk when the Mean Is not Finite

We consider the sums S _n =ξ₁+?+ξ _n of independent identically distributed random variables. We do not assume that the ξ's have a finite mean. Under subexponential type conditions on distribution of the summands, we find the asymptotics of the probability P{M>x} as x→∞, provided that M=sup {S _n ,n≥1} is a proper random variable. Special attention is paid to the case of tails which are regularly varying at infinity. We provide some sufficient conditions for the integrated weighted tail distribution to be subexponential. We supplement these conditions by a number of examples which cover both the infinite- and the finite-mean cases. In particular, we show that the subexponentiality of distribution F does not imply the subexponentiality of its integrated tail distribution F ^I.     

Explicit Bounds for the Return Probability of Simple Random Walks

We give an exact computation of the second order term in the asymptotic expansion of the return probability, P_2n^d(0,0), of a simple random walk on the d-dimensional cubic lattice. We also give an explicit bound on the remainder. In particular, we show that P_2n^d(0,0) < 2 (d/4n)^d/2 where n M=M(d) is explicitly given.