二元混合型指数分布的识别性及其应用

对新提出的一类二元混合型指数分布和其他三类二元混合型指数分布,讨论了它们的分布识别问题,即记z=min（x1,x2）,I=i,当z=xi;记U-max（＆,x2）,J=j,当U=Xi;已知（Z,I）或（U,J）的分布,求（X1,X2）分布的唯一性问题．给出了（x1,x2）服从Marshall-Olkin型指数分布时,有关寿险中Z及u的精算现值的2个公式．     

THE CLASSIFICATION OF NILPOTENT LEIBNIZ 3-ALGEBRAS

The notions of the nilpotent and the strong-nilpotent Leibniz 3-algebras are defined. And the three dimensional two-step nilpotent, strong-nilpotent Leibniz 3-algebras are classified.     

关于再保险效应的注记

本文在Sparre Anderson模型中采用超额损失再保险与成数分保混合的策略,其中成数分保再保险费按照原始条款计算,超额损失再保险费按Esscher保费原则计算。通过调整系数来研究再保险的效应,将调整系数看作自留额水平的函数,证明了在M充分大时保险人的调整系数关于自留额水平M单调增加,在一定程度上有利于保险公司确定更合理的自留额水平M。     

两步投影对变分不等方程组解的迭代逼近

本文研究Hilbert空间中两步投影方法及其对变分不等方程组解具误差的迭代序列的收敛性.本文结果发展和改进了Verma等人的最新结果.     

Estimation of a mean in the presence of an extreme observation

The performance of Anscombe, semi-Winsorization and Winsorization (A, S and W) rules for dealing with extreme observations are investigated for observations from N(μ, σ²) and the simple case where it is assumed that at most one observation in the sample may be biased, arising from N(μ + aσ, σ²) and the primary objective is to estimate μ when σ is unknown. Each of these rules is separately treated in terms of the estimated standard deviation, range and interquartile range. A Monte Carlo method is used to evaluate certain expectation integrals that arise in the computations. We give the results for sample sizes n = 6, 8, 10, 12, 14, 16, 20, 30, 40, 50, 60, 80, 100 of determining the constants necessary to give ‘premiums’ of 0.01 and 0.05 for each of the rules. The performance of the rules is measured in terms of ‘protection’. Features of the resulting tables are discussed.     

风险理论中破产模型的若干结果

本文分连续时间和离散时间两种情况对古典的破产模型做了改进和推广 ,并给出了统一的破产概率的表达式 .     

Numerical solution of a nonlinear parabolic control problem by a reduced SQP method

We consider a control problem for a nonlinear diffusion equation with boundary input that occurs when heating ceramic products in a kiln. We interpret this control problem as a constrained optimization problem, and we develop a reduced SQP method that presents for this problem a new and efficient approach of its numerical solution. As opposed to Newton's method for the unconstrained problem, where at each iteration the state must be computed from a set of nonlinear equations,in the proposed algorithm only the linearized state equations need to be solved. Furthermore, by use of a secant update formula, the calculation of exact second derivatives is avoided. In this way the algorithm achieves a substantial decrease in the total cost compared to the implementation of Newton's method in [2]. Our method is practicable with regard to storage requirements, and by choosing an appropriate representation for the null space of the Jacobian of the constraints we are able to exploit the sparsity pattern of the Jacobian in the course of the iteration. We conclude with a presentation of numerical examples that demonstrate the fast two-step superlinear convergence behavior of the method.     

ZnO-SnO2透明导电薄膜的制备及性能研究

采用二步成胶工艺制备ZnO-SnO₂透明导电薄膜,应用X射线衍射、原子力显微镜、紫外-可见分光光度计、薄膜分析仪及四探针仪等对薄膜的结构、表面微观形貌、透过率和导电性能进行表征.结果表明,锌锡摩尔比为9/12,退火温度为500 ℃时,薄膜的透过率达90%,电阻率为3.15×10-3 Ω·cm.与其它工艺相比,二步成胶工艺所制备出的ZnO-SnO₂透明导电薄膜性能优异.     

Asymptotic theory for the empirical Haezendonck–Goovaerts risk measure

Haezendonck–Goovaerts risk measures is a recently introduced class of risk measures which includes, as its minimal member, the Tail Value-at-Risk (T-VaR)—T-VaR arguably the most popular risk measure in global insurance regulation. In applications often one has to estimate the risk measure given a random sample from an unknown distribution. The distribution could either be truly unknown or could be the distribution of a complex function of economic and idiosyncratic variables with the complexity of the function rendering indeterminable its distribution. Hence statistical procedures for the estimation of Haezendonck–Goovaerts risk measures are a key requirement for their use in practice. A natural estimator of the Haezendonck–Goovaerts risk measure is the Haezendonck–Goovaerts risk measure of the empirical distribution, but its statistical properties have not yet been explored in detail. The main goal of this article is to both establish the strong consistency of this estimator and to derive weak convergence limits for this estimator. We also conduct a simulation study to lend insight into the sample sizes required for these asymptotic limits to take hold.     

Kernel-type estimator of the reinsurance premium for heavy-tailed loss distributions

In this paper, we generalize the classical estimator of the reinsurance premium for heavy-tailed loss distributions with a kernel-type estimator. Since this estimator exhibits a bias, we propose its bias-reduced version by using a least-squares method. The asymptotic normality of the proposed estimators is established under suitable assumptions. A small simulation study is carried out to prove the performance of our approach.