一类相依索赔风险模型的破产概率问题研究

考虑一种相依索赔风险模型,其中每次索赔发生时根据索赔额的大小可随机产生一延迟的副索赔.采用L ap lace变换方法,给出了索赔额服从轻尾分布时的最终破产概率,并研究了重尾分布时最终破产概率的极限上下界.     

重尾索赔下的一类相依风险模型的若干问题

本文研究了重尾索赔下的一类相依风险模型,得到了破产概率的尾等价式及索赔盈余过程大偏差的渐近关系式.在该模型中,一索赔到达过程是Poisson过程,另一索赔到达过程为其p-稀疏过程.     

一类索赔相依二元风险模型的破产概率问题研究

考虑一种相依索赔风险模型,模型中假设每次主索赔可随机产生一延迟的副索赔,采用Laplacc变换方法,给出了索赔额服从轻尾分布时的最终破产概率,并研究了重尾分布时最终破产概率的渐进式.     

复合二项分布下多险种风险模型的大偏差

考虑了重尾分布的多险种复合二项风险模型,在索赔额分布服从一致变化尾时,得到了其总索赔过程和总索赔盈利过程的大偏差,推广了经典复合二项风险模型的结论.     

广义负相依一般风险模型中有限时破产概率的估计及数值模拟

本文研究了一类带利率的重尾相依风险模型, 其中索赔额是一列上广义负相依随机变量, 索赔到达过程是一般的非负整值过程, 并且独立于索赔额序列, 保费收入过程是一个一般的非负非降随机过程. 我们考虑了两种情况, 其一是索赔额、索赔到达过程及保费收入过程相互独立, 其二是累积折现保费收入总量的尾概率可以被索赔额的尾概率高阶控制, 得到了保险公司有限时破产概率的渐近估计,并且给出了相应的数值模拟, 验证了理论结果的合理性.     

一类时间相依复合更新风险模型损失过程的尾概率渐近估计

考虑一类复合相依更新风险模型,一次事故引发多次索赔.假设索赔次数与索赔时刻相依,同一事故引起的索赔额是宽上限相依(widely upper orthant dependent)且服从重尾分布.得到该风险模型损失过程的精细大偏差和有限时破产概率的渐近估计.     

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.     

一类重灾风险模型的生存概率

无法预料的索赔是导致保险公司破产的一大因素,这种情况引起的索赔大多不是同分布的.因此论文从这一实际情况出发,在Sparre Andersen风险模型的基础上建立了广义更新风险模型,并对重尾索赔分布F∈S*给出了生存概率的局部等价式和破产概率的尾等价式.文章结果刻画了特殊巨额索赔对公司运营状况的影响,对公司运营策略提供了理论基础.本文结果包含、推广并改进了许多已知结果,与经典结果相比,显示出了模型的优越性.     

一类相依索赔离散风险模型的有限时间破产概率估计

本文研究一类具有相依索赔及重尾索赔噪声项的离散风险模型有限时间破产概率.在该模型中,索赔额服从具有独立同分布噪声项的单边线性过程;由保险公司的风险投资和无风险投资导致的随机折现因子与单边线性过程的噪声项相独立;保险公司的保费率是恒定的常数.当单边线性过程的噪声项服从重尾分布时,本文得到该离散风险模型有限时间破产概率的渐近估计.     

负相协重尾随机变量随机和的中偏差

本文讨论了具有重尾负相协索赔的经典风险模型．通过一种改进后的大偏差技术，给出了索赔总量过程的中偏差结果：所获结果推广了独立索赔情形下的相应的结果．     

关于伽马分布及相关分布性质的一点研究

主要研究伽马分布的性质,并通过对伽马分布可加性的研究.得到由指数分布通过伽马分布构造卡方分布和均匀分布的方法,通过本文可以加深对伽马分布和其它常见连续性分布关系的认识.     

截尾分布与截尾样本的统计分析

[1]中给出了样本容量n已知的(n,r）定时截尾不完全样本的分析方法。在[2]中我们讨论了n未知的（n,r）定时截尾不完全样本并较好地处理了晚截尾的接近完全样本的分析。本文中我们相应于截尾样本，引入截尾分布，通过用截尾样本拟合截尾分布而得到寿命T的整体分布。此方法无论对n已知还是n未知，对早截尾还是晚截尾的（n,r）定时截尾样本都适用。并且此方法还可推广到对其它形式的不完全样本的处理。     

A Characterization of the Riesz Probability Distribution

Bobecka and Wesolowski (Studia Math. 152:147–160, [2002]) have shown that, in the Olkin and Rubin characterization of the Wishart distribution (see Casalis and Letac in Ann. Stat. 24:763–786, [1996]), when we use the division algorithm defined by the quadratic representation and replace the property of invariance by the existence of twice differentiable densities, we still have a characterization of the Wishart distribution. In the present work, we show that when we use the division algorithm defined by the Cholesky decomposition, we get a characterization of the Riesz distribution.     

A Review of Results on Sums of Random Variables

Sums of random variables arise naturally in wireless communications and related areas. Here, we provide a review of the known results on sums of exponential, gamma, lognormal, Rayleigh and Weibull random variables. A discussion is provided of two applications. We expect that this review could serve as a useful reference and help to advance further research in this area.     

Asymptotic Distribution of a Kind of Dirichlet Distribution

The Dirichlet distribution that we are concerned with in this paper is very special, in which all parameters are different from each other. We prove that the asymptotic distribution of this kind of Dirichlet distributions is a normal distribution by using the central limit theorem and Slutsky theorem.     

New examples of heavy-tailed O-subexponential distributions and related closure properties

Let L and S denote the classes of distributions with long tails and subexponential tails respectively. Let OS denote the class of distributions with O-subexponential tails, which means the distributions with the tails having the same order as the tails of their 2-fold convolutions. In this paper, we first construct a family of distributions without finite means in L∩OS?S. Next some distributions in L∩OS?S, which possess finite means or even finite higher moments, are also constructed. In connection with this, we prove that the class OS is closed under minimization of random variables. However, it is not closed under maximization of random variables.     

Compound negative binomial shared frailty models for bivariate survival data

In this paper, we introduce a new shared frailty model called the compound negative binomial shared frailty model with three different baseline distributions namely, Weibull, generalized exponential and exponential power distribution. To estimate the parameters involved in these models we adopt Markov Chain Monte Carlo (MCMC) approach. Also we apply these three models to a real life bivariate survival data set of McGrilchrist and Aisbett (1991) related to kidney infection and suggest a better model for the data.     

On bounds for the mode and median of the generalized hyperbolic and related distributions

Except for certain parameter values, a closed form formula for the mode of the generalized hyperbolic (GH) distribution is not available. In this paper, we exploit results from the literature on modified Bessel functions and their ratios to obtain simple but tight two-sided inequalities for the mode of the GH distribution for general parameter values. As a special case, we deduce tight two-sided inequalities for the mode of the variance-gamma (VG) distribution, and through a similar approach we also obtain tight two-sided inequalities for the mode of the McKay Type I distribution. The analogous problem for the median is more challenging, but we conjecture some monotonicity results for the median of the VG and McKay Type I distributions, from we which we conjecture some tight two-sided inequalities for their medians. Numerical experiments support these conjectures and also lead us to a conjectured tight lower bound for the median of the GH distribution.     

正态分布和瑞利分布混合情形下的参数估计及分类问题

学者往往用单一的分布模拟和拟合杂波,如正态分布、瑞利分布和威布尔分布等。然而在实际中,雷达杂波由多种类型的杂波组成,单一分布通常不能精确刻画雷达杂波规律,因此,应用混合分布模型对雷达杂波数据建模更准确。本文考虑用正态分布和瑞利分布的混合分布拟合杂波,并应用矩估计方法和基于EM算法的极大似然估计方法估计模型参数,最后,应用最大后验概率分类准则验证2种估计方法的分类准确率。通过数据模拟,得出极大似然估计的效果和分类准确率都要优于矩估计的估计效果和分类准确率。     

双曲分布及其在VaR模型分析中的应用

传统的计算V aR的R iskM etrics方法不能对市场风险分布的“厚尾”现象给出较为满意的刻画和计算方法.本文引入双曲分布及其算法并将双曲分布应用到V aR模型的计算之中,事实上通过对股票市场的实证研究表明,股票市场数据呈厚尾现象,用双曲分布对数据的拟合要比R iskM etrics方法假定的正态分布更符合金融市场数据的实际情况,故本文的结论与方法对金融风险管理和其他金融建模是有价值的.