一类随机加权和的渐近性及其应用

本文考虑随机加权和及其最大值尾概率的渐近性,其中增量{X_i,i≥1}为一列独立同分布的实值随机变量,权重{θ_i,i≥1}为另一列非负的随机变量,并且两列随机变量满足某种相依结构.在增量的共同分布F属于控制变换分布族的条件下,我们得到了随机加权和及其最大值尾概率的弱渐近等价估计.特别地,当F属于一致变换分布族时,得到了渐近等价估计.最后,我们将该结果应用于破产概率的渐近估计.     

多元模型的长尾相依大偏差的下界

研究了在多元模型中的服从长尾分布且带有负相依的随机变量和的尾概率,在给定的一些条件下通过采用多元大偏差的方法得到了随机变量的非随机和和随机和的大偏差的下界,推广了相应的独立同分布情形下的结论.     

次指数随机变量最大和的大偏差概率及其在有限时间破产概率中的应用

对于由独立同分布的标准均匀分布随机变量中心化的次指数随机变量序列,对于其部分和的最大值 建立了一个大偏差概率的渐近关系.该结果扩展了Korshunov相应的结论. 作为应用, 将Tang的结果,即关于有限时间破产概率的一致渐近估计,由一致变化分布族推广到了整个强次指数族.     

宽上限相依的随机变量和的精确大偏差

通过研究了长尾上的带宽上限相依的随机变量和的精确大偏差,利用经典大偏差的方法,得到了非随机和和随机和的两种渐近结果.     

L族END的多元部分和的精确大偏差下界

从保险的实际出发,研究服从长尾分布族(L族)上的多元风险模型中随机变量序列的部分和的精确大偏差,其中假设随机变量序列是一列延拓负相依(END)的、同分布的随机变量序列,利用基于求L族的精确大偏差的方法得到了随机变量部分和的渐近下界.     

长尾宽上限相依的不同分布的随机变量和的精确大偏差

研究了服从长尾分布族上的随机变量和的精确大偏差问题,其中假设代表索赔额的随机变量序列是一列宽上限相依的、不同分布的随机变量序列。在给定一些假设条件下,得到了部分和与随机和的两种一致渐近结论。     

非负不同分布负相协随机变量下的精细大偏差（英文）

本文研究非负,不同分布,负相协随机变量的精细大偏差问题.在相对较弱的条件下,重点解决了非随机和的精细大偏差的下限问题,得到相对应的随机和的一致渐近结论.同时,对复合更新风险模型进行了深入的讨论,在一定的条件之下将其简化为一般的更新模型,并将所得相关的精细大偏差的结论应用到更为实际的复合更新风险模型中,验证了其理论与实际价值.除此之外,本文的研究还表明,随机变量间的这种相依关系对精细大偏差的最终结果的影响并不大.     

D族END的随机变量和的精确大偏差

研究了非随机和的S_n=∑_i=1ⁿ X_i,n≥1的精确大偏差的问题,这里{X_i,i≥1}是服从控制变化尾分布族（D族）的非负的、END的随机变量,但不必是同分布的.在给定的一些假设条件下,得到了非随机和的渐近关系,推广了相应的独立同分布情形下的结论.     

非负不同分布负相伴随机变量下的精细大偏差

研究非负,不同分布,负相伴随机变量的精细大偏差问题.在相对较弱的条件下,重点解决非随机和的精细大偏差的下限问题,得到相对应的随机和的一致渐近结论.同时,对复合更新风险模型进行深入的讨论,在一定的条件之下将其简化为一般的更新风险模型,并将所得相关的精细大偏差的结论应用到更为实际的复合更新风险模型中,验证其理论与实际价值.除此之外,本文的研究还表明,随机变量间的这种相依关系对精细大偏差的最终结果的影响并不大.     

渐近正态随机变量函数的极限分布

基于渐近正态随机变量,导出随机变量函数极限分布的两个一般性理论结果.作为应用,证明了渐近正态随机变量一系列具体函数的极限分布,其中包括泊松随机变量平方根的渐近正态性,以及随机变量部分和在正则化常数是随机变量情况下的渐近正态性.     

Precise Large Deviations for Long-Tailed Distributions

In this paper, we investigate the precise large deviations for sums of independent identically distributed random variables with heavy-tailed distributions. We prove asymptotic relations for non-random sums and for random sums of random variables with long-tailed distributions. We apply the results on two useful counting processes, namely, renewal and compound-renewal processes.     

Precise large deviations for consistently varying-tailed distributions in the compound renewal risk model

We investigate precise large deviations for heavy-tailed random sums. We prove a general asymptotic relation in the compound renewal risk model for consistently varying-tailed distributions. This model was introduced in [Q. Tang, C. Su, T. Jiang, and J.S. Zang, Large deviation for heavy-tailed random sums in compound renewal model, Stat. Probab. Lett., 52:91–100, 2001] as a more practical risk model. The proof is based on the inequality found in [D. Fuk and S.V. Nagaev, Probability for sums of independent random variables, Theory Probab. Appl., 16:600–675, 1971].     

独立重尾随机变量随机和的大偏差估计

本文研究了一类独立重尾随机变量随机和S（t)∧=∑k=1^N(t)Xk,t≥0的大偏差概率，其中{N(t),t≥0}是一放大晨负整数值随机变量；{Xn,n≥1}是非负，独立随机变量序列，并与{N(t),t≥０}独立。本文的结果将{Xn,n≥１}为独立同分布情形推广到了独立不同分布情形。     

A distributional limit law for the continued fraction digit sum

We consider the continued fraction digits as random variables measured with respect to Lebesgue measure. The logarithmically scaled and normalised fluctuation process of the digit sums converges strongly distributional to a random variable uniformly distributed on the unit interval. For this process normalised linearly we determine a large deviation asymptotic. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Large deviations for product of sums of random variables

In this paper, we obtain sample path and scalar large deviation principles for the product of sums of positive random variables. We study the case when the positive random variables are independent and identically distributed and bounded away from zero or the left tail decays to zero sufficiently fast. The explicit formula for the rate function of a scalar large deviation principle is given in the case when random variables are exponentially distributed.     

On large deviations for sums of random vectors in Rk

The paper deals with limit theorems for probabilities of large deviations for sums of independent identically distributed random vectors. We give more detailed bounds for the remainder in von Bahr's limit theorem. New asymptotic formulas for probabilities of large deviations on the outside of balls are established.     

Asymptotic Expansions of Large Deviations for Sums of Nonidentically Distributed Random Variables

The work is designated for obtaining asymptotic expansions and determination of structures of the remainder terms that take into consideration large deviations both in Cramer zones and Linnik power zones for the distribution function of sums of independent nonidentically distributed random variables (r.v.). In this scheme of summation of r.v., the results are obtained first by mainly using the general lemma on large deviations considering asymptotic expansions for an arbitrary r.v. with regular behaviour of its cumulants [11]. Asymptotic expansions in the Cramer zone for the distribution function of sums of identically distributed r.v. were investigated in the works [1,2]. Note that asymptotic expansions for large deviations were first obtained in the probability theory by J. Kubilius [3].     

Multidimensional large deviation local limit theorems

An earlier paper by the author ([4], 97–114) established large deviation local limit theorems for arbitrary sequences of real valued random variables. This work showed clearly the connection between the Cramér series and large deviation rates. In this article we present large deviation local limit theorems for arbitrary multidimensional random variables based solely on conditions imposed on their moment generating functions. These results generalize the theorems of [12], 100–106) for sums of independent and identically distributed random vectors.     

Estimate of Distributions of Sums of Independent Random Variables

We investigate necessary and sufficient conditions under which one estimate of exponential type is valid for large deviation probabilities of sums of independent identically distributed random variables. Bibliography: 3 titles.