Optimal reinsurance under risk and uncertainty

This paper deals with the optimal reinsurance problem if both insurer and reinsurer are facing risk and uncertainty, though the classical uncertainty free case is also included. The insurer and reinsurer degrees of uncertainty do not have to be identical. The decision variable is not the retained (or ceded) risk, but its sensitivity with respect to the total claims. Thus, if one imposes strictly positive lower bounds for this variable, the reinsurer moral hazard is totally eliminated.Three main contributions seem to be reached. Firstly, necessary and sufficient optimality conditions are given in a very general setting. Secondly, the optimal contract is often a bang–bang solution, i.e., the sensitivity between the retained risk and the total claims saturates the imposed constraints. Thirdly, the optimal reinsurance problem is equivalent to other linear programming problem, despite the fact that risk, uncertainty, and many premium principles are not linear. This may be important because linear problems may be easily solved in practice, since there are very efficient algorithms.     

Stochastic comparison of aggregate claim amounts between two heterogeneous portfolios and its applications

The aggregate claim amount in a particular time period is a quantity of fundamental importance for proper management of an insurance company and also for pricing of insurance coverages. In this paper, we show that the proportional hazard rates (PHR) model, which includes some well-known distributions such as exponential, Weibull and Pareto distributions, can be used as the aggregate claim amount distribution. We also present some conditions for the use of exponentiated Weibull distribution as the claim amount distribution. The results established here complete and extend the well-known result of Khaledi and Ahmadi (2008).     

Some properties and applications of cumulative Kullback–Leibler information

The cumulative Kullback–Leibler information has been proposed recently as a suitable extension of Kullback–Leibler information to the cumulative distribution function. In this paper, we obtain various results on such a measure, with reference to its relation with other information measures and notions of reliability theory. We also provide some lower and upper bounds. A dynamic version of the cumulative Kullback–Leibler information is then proposed for past lifetimes. Furthermore, we investigate its monotonicity property, which is related to some new concepts of relative aging. Moreover, we propose an application to the failure of nanocomponents. Finally, in order to provide an application in image analysis, we introduce the empirical cumulative Kullback–Leibler information and prove an asymptotic result. Copyright © 2015 John Wiley & Sons, Ltd.     

基于证据推理的混合型多指标群组决策方法

针对决策方案的属性值为语言评价等级和区间灰数的灰色多指标群组决策问题,提出一种基于证据推理的灰色多指标群组决策方法.首先,根据语言评价信息的概率分布和效用值等价原理确定定性指标和定量指标的信用结构,进而得到群体等级信用结构决策矩阵,然后,依据证据推理方法,对群组评价信息进行合成,求出各方案在各等级的信任度,最后,利用期望方差排序方法确定整个方案集的排序.具体算例表明方法合理有效.     

嵌套病例对照研究中的两步估计方法（英）

本文讨论嵌套病例对照研究中相对危险率的估计问题，引入了相对危险率的两步估计，并在一般嵌套病例对照抽样的假设下讨论了相对危险率的两步估计的相合性问题，最后给出了几个例子。     

属性区间识别理论模型研究及其应用

根据程乾生教授创立的属性识别理论模型[1] ,提出了属性测度区间的概念 ,建立了属性区间识别理论模型 ,并在自然科学学术期刊质量评价中进行了应用 .     

教师课堂教学质量的定量分析

本文根据学生对教师课堂教学质量的评分建立指标评估体系 ,得到一次量化数据 .对一次量化数据进行统计分析 :粗差剔除 ,计算标准分 ,得到被评估者在各总体中的相对位置 .在此基础上 ,采用二次量化模型 ,最后得到在全校范围内的教师得分排序 .     

Birnbaum‐Saunders distribution: A review of models,analysis, and applications

Birnbaum and Saunders introduced a two‐parameter lifetime distribution to model the fatigue life of a metal, subject to cyclic stress. Since then, extensive work has been done on this model providing different interpretations, constructions, generalizations, inferential methods, and extensions to bivariate, multivariate, and matrix‐variate cases. More than 200 papers and one research monograph have already appeared describing all these aspects and developments. In this paper, we provide a detailed review of all these developments and, at the same time, indicate several open problems that could be considered for further research.     

一种基于Rough集理论的最小风险决策规则提取方法

波兰科学家Zdzisilaw Pawlak将概率统计中的Bayesian公式与Rough集模型相结合建立基于决策理论的概率模型，给出了一系列的决策规则及相关讨论。在此基础上结合在银行信贷分析的实际例子描述了基于Rough集理论的最小风险决策规则提取方法及它的实际应用，可以看出这为非结构化风险决策问题提供了一条很好的辅助决策的途径。