预测死亡率分布的一个最小叉熵模型

被保人的死亡率分布是确定寿险费率的一个重要依据,而根据其生活的环境、时间预测被保人的死亡率是保险精算研究中的一个热点问题.基于最小叉熵原理,建立了预测被保人死亡率分布的一个模型--最小叉熵模型,该模型以叉熵函数作为目标函数,以被保人的预期寿命作为约束条件,通过最小化叉熵预测被保人的死亡率.以从事特殊职业的被保人为研究对象,通过最小叉熵模型计算了该类人的死亡率.该方法计算简便,具有较好的客观性和实用性,为死亡率预测研究提供了一种有效的新方法.     

Linex损失下Rayleigh分布参数倒数的Bayes估计

在Linex损失函数L(θ,δ)=ec(δ-θ)-c(δ-θ)-1,c>0下,给出Rayleigh分布的尺度参数倒数的唯一Bayes估计δB(X)=-1/clnE(e-cθ│X)=(n α)/cln(1 c/(λ T)),多层Byaes估计δ∧B(X)=-1/cln,和容许性估计的一般形式Sln(1 c/(d T)).     

AutoCAD在直线度误差实验教学中的应用

运用AutoCAD软件绘制出折线图,根据最小包容原则,作出两条平行直线,再通过两点,标注出尺寸线,该尺寸线的长度就是所求的直线度误差值。传统方法是通过手工作图,用米尺测出两点之间的距离,缺点是误差大,而且容易产生误判。运用AutoCAD软件作图,具有简单、美观、精确等特点。     

基于最小扰动相关去噪法股票投资组合风险优化

金融相关矩阵的计算是构建金融投资组合的基础.为解决金融相关矩阵的“维数灾祸”问题,进而促进金融投资组合风险的优化,受基于随机矩阵理论(RMT)和特征向量的Krzanowski稳定性的KR去噪法的启发,对收益相关矩阵特征值增大时的特征向量最小扰动进行了数学推导,并将以该扰动衡量的特征向量的Krzanowski稳定性引入到RMT去噪法中,进而建立对金融收益相关矩阵去噪的KRMIN方法. KRMIN法对KR法的算法进行了两方面的优化.一方面, KRMIN法对KR法的特征值设定方法进行了扩展; 另一方面, KRMIN法采用模拟退火算法计算特征值.理论研究表明,由于在收益相关矩阵特征向量的稳定性和特征值算法准确性上的优势, KRMIN方法将获得比KR法更好的组合风险优化效果.通过bootstrap方法,开展了将LCPB法、PG+法、KR法和KRMIN法用于不同数量股票的投资组合优化的实证研究.结果表明: LCPB法、PG+法、KR法和KRMIN法都能通过股票收益相关矩阵去噪而带来投资组合风险的优化;基于收益相关矩阵特征向量的Krzanowski稳定性的KR法和KRMIN法的组合风险比其他两种方法更低;由KRMIN法得到的收益相关矩阵的特征向量稳定性和组合风险优化效果好于KR法.     

极大局部边连通和超级局部边连通二部有向图的邻域条件

本文主要证明了对于n阶二部有向图D，当最小度δ≥3,对任意同部顶点x，y,有min{|N⁺(x)∪N⁺(y)|，|N^-(x)∪N^-(y)|}≥(n+3)/4]时,D为极大局部边连通的；当最小度δ≥4,对任意同部顶点x，y,有min{|N⁺(x)∪N⁺(y)|,|N^-(x)∪N^-(y)|}>(n/4)+1时,D为超级局部边连通的。我们证明了条件的最好可能性及结果与原有结果的独立性。     

求最小生成树的矩阵算法

最小生成树问题是运筹学网络优化中一个常见的基本问题．提出了一种新的求最小生成树的矩阵算法，此算法可以不必在原图上进行操作而得到最小生成树，过程简单易懂．     

最小自然数原理在初等数论中的几个应用

利用最小自然数原理简化了一些重要的数论问题的证明,阐述了最小自然数原理的重要性和应用问题.     

Receding horizon H∞ control for discrete-time Markovian jump linear systems

Receding horizon H∞ control scheme which can deal with both the H∞ disturbance attenuation and mean square stability is proposed for a class of discrete-time Markovian jump linear systems when minimizing a given quadratic performance criteria. First, a control law is established for jump systems based on pontryagin’s minimum principle and it can be constructed through numerical solution of iterative equations. The aim of this control strategy is to obtain an optimal control which can minimize the cost function under the worst disturbance at every sampling time. Due to the difficulty of the assurance of stability, then the above mentioned approach is improved by determining terminal weighting matrix which satisfies cost monotonicity condition. The control move which is calculated by using this type of terminal weighting matrix as boundary condition naturally guarantees the mean square stability of the closed-loop system. A sufficient condition for the existence of the terminal weighting matrix is presented in linear matrix inequality (LMI) form which can be solved efficiently by available software toolbox. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method.     

Vegetation effects on mean daily maximum and minimum surface air temperatures over China

Changes in the daily maximum (Tmax) and minimum (Tmin) surface air temperatures and the associated temperature extremes have severe consequences on human society and the natural environment. In this study, we assess vegetation effects on mean Tmax and Tmin over China by computing a vegetation feedback parameter using the satellite-sensed Normalized Difference Vegetation Index (NDVI) and observed temperatures for the period 1982–2002. In all seasons, vegetation exerts a much stronger forcing on Tmax than on Tmin, and thus has a substantial effect on the diurnal temperature range (DTR) over China. Significant positive feedbacks on Tmax and the DTR occupy many areas of China with the feedback parameters exceeding 1°C (0.1 NDVI)–1, while significant negative effects only appear over the summertime climatic and ecological transition zone of northern China and some other isolated areas. Also, the vegetation feedbacks are found to vary with season. In areas where significant feedbacks occur, vegetation contributes to typically 10%–30% of the total variances in Tmax, Tmin, and the DTR. These findings suggest that vegetation memory offers the potential for improving monthly-to-seasonal forecasting of Tmax and Tmin, and the associated temperature extremes over China. Meanwhile, the limitations and uncertainties of the study should be recognized.