带干扰的双险种风险模型下的大偏差问题

考虑了一类带干扰的双险种风险模型,得到了索陪额分布属于εRV族时,索赔盈利过程的两个偏差结果.     

复合二项过程风险模型的精细大偏差及有限时间破产概率

讨论基于客户到来的复合二项过程风险模型.在该风险模型中,假设索赔额序列是独立同分布的重尾随机变量序列,不同保单发生实际索赔的概率可以不同,则在索赔额服从ERV的条件下,得到了损失过程的精细大偏差;进一步地,得到了有限时间破产概率的Lundberg极限结果.     

常利率复合二项双险种风险模型的研究

提出了含利率因素的复合二项双险种风险模型,并在有关假设的基础上,给出了此模型下保险公司稳定经营的必要条件;证明了索赔时刻的盈余过程是一马氏过程和调节系数的存在性,并采用递归方法得到了模型的破产概率的上界估计.     

变保费率的扰动多险种模型总索赔盈余的大偏差

考虑变保费率的扰动多险种更新模型.在索赔额分布属于一致变化类的条件下,给出总索赔盈余过程的精致大偏差.     

广义复合二项风险模型的若干大偏差结果

This paper is a further investigation of large deviation for partial and random sums of random variables, where {Xn,n ≥ 1} is non-negative independent identically distributed random variables with a common heavy-tailed distribution function F on the real line R and finite mean μ∈ R. {N（n）,n ≥ 0} is a binomial process with a parameter p ∈ （0,1） and independent of {Xn,n ≥ 1}; {M（n）,n ≥ 0} is a Poisson process with intensity λ 〉 0, Sn = ΣNn i=1 Xi-cM（n）. Suppose F ∈ C, we futher extend and improve some large deviation results. These results can apply to certain problems in insurance and finance.     

二维复合二项风险模型的精细大偏差

考虑保费随机收取,且索赔过程是保费收取的稀疏过程的二维风险模型,在索赔额的分布是一致变化尾分布并且copula相依时,得到其总索赔和总盈余过程随机和的精细大偏差,推广了相关文献的结论.     

一类离散双险种风险模型

本文推广了[1]的离散双险种风险模型,讨论了两类险种的索赔均为负二项随机序列的情形,得到了最终破产概率的Lundberg不等式以及一般表达式.     

多险种风险模型的再保险

本文在考虑保险公司实际经营过程的基础上,建立了一个索赔到达为齐次Poisson过程且含有随机干扰项的多险种风险模型,分别讨论了其在比例再保险和超额再保险两种情况下调节系数R的上下界,得到索赔额服从指数分布时调节系数R与比例再保险比例系数α,以及调节系数R与超额再保险的免赔额M的关系式,并分别给出算例,得出和经典风险模型再保险一致的结论.     

一个复合风险模型的引入及其大偏差估计的建立

本文研究了关于独立随机和精大偏差的估计问题,改进了文献[4,7]的结果。首先我们引入了一个比过去工作更现实复合更新风险模型,然后在该模型下建立了与文献中完全相同的精大偏差结果。     

一类多险种风险过程的破产概率

由于保险公司风险经营规模的不断扩大,考虑到用单一险种的风险模型来描述风险经营过程的局限性,本文建立了多险种风险模型,并对其中一类特殊的风险模型的破概率进行了研究,给出了初始资本为0时破产概率Ψ（0）的明确表达式,以及初始资本为μ的破产概率Ψ（μ）的近似估计和在某些特殊情形下Ψ（μ）的明确表达式。     

Large Deviations for the Markov Binomial Distribution

Considering the Markov binomial distribution, we study large deviations for the Poisson approximation. Apart from the standard choice of parameters, we use the approach where the parameter of approximation depends on the argument of the approximated distribution function.     

多维风险模型中独立和的局部大偏差

In this paper, we study the case of independent sums in multi-risk model. Assume that there exist k types of variables. The ith are denoted by {Xij, j ≥ 1}, which are i.i.d.with common density function fi(x) ∈ OR and finite mean, i = 1,..., k. We investigate local large deviations for partial sums k i=1Sni= k i=1 nij=1Xij.     

Finite Time Ruin Probabilities and Large Deviations for Generalized Compound Binomial Risk Models

In this paper, we extend the classical compound binomial risk model to the case where the premium income process is based on a Poisson process, and is no longer a linear function. For this more realistic risk model, Lundberg type limiting results for the finite time ruin probabilities are derived. Asymptotic behavior of the tail probabilities of the claim surplus process is also investigated.     

复合更新风险模型中负相依索赔额下的精细大偏差（英文）

本文考虑了在复合更新风险模型当中,负相依索赔额情形下与之相关的精细大偏差的若干问题.文中假设{X_n,n≥1}是一列负相依的随机变量,其对应分布列为{F_n,n≥1},并假定F_n的右尾分布等同于某个具有一致变化尾的分布.根据所得的结果试图建立与经典大偏差相似的结论,并将其应用到改进后的复合更新风险模型当中.     

对于复合更新模型重尾随机和的大偏差结果的一个改进

本文在一个相对较弱的假设之下，得到了复合更新风险模型中重尾随机和的精确大偏差等价式，该结果对文[1]中的结果进行了改进。     

Theorems of Large Deviations in the Approximation by the Compound Poisson Distribution

The paper is devoted to the research of large deviation probabilities in the approximation by compound Poisson law.     

Asymptotic Expansion for the Distribution Density Function of the Compound Poisson Process in Large Deviations

The paper is devoted to obtaining the asymptotic expansion and determination of the structure of the remainder term taking into consideration large deviations in the Cramér zone for the distribution density function of the standardized compound Poisson process. Following Deltuvien? and Saulis (Acta Appl Math 78:87–97, 2003. doi: 10.1023/A:1025783905023; Lith Math J 41:620–625, 2001) and Saulis and Statulevi?ius [Limit theorems for large deviations. Mathematics and its applications (Soviet Series), vol 73, pp 154–187, Kluwer, Dordrecht, 1991], the solution to the problem is achieved by first using a general lemma presented by Saulis (see Lemma 6.1 in Saulis and Statulevi?ius 1991, p. 154) on the asymptotic expansion for the density function of an arbitrary random variable with zero mean and unit variance and combining methods for cumulants and characteristic functions. By taking into consideration the large deviations in the Cramér zone for the density function of the standardized compound Poisson process, the result for the asymptotic expansion extends the asymptotic expansions for the density function of the sums of non-random number of summands (Deltuvien? and Saulis 2003, 2001).