回收量不确定的垃圾回收网络优化模型及算法

考虑到实际垃圾回收网络中回收中心日回收量的不确定性特征,以及经城市垃圾处理中心处理后的垃圾被再利用的可能性,建立了一类新的日回收量不确定的垃圾回收处理网络优化模型,分别就全部或部分垃圾能够被再利用两种情形,在假定日回收量为三角模糊量的条件下,利用模糊机会约束规划的方法推导了所建立的不确定性优化模型的确定型等价类.对给定的置信水平,利用LINGO软件能够直接求解所得到的确定性模型.案例研究证实了模型和方法的有效性,应用参数的灵敏度分析得到:目标和不同约束的置信水平对最优运输方案和最小运输成本均具有不同影响.     

非等间距NGM(1,1,k)模型及其应用

针对近似非齐次指数律的非等间距序列预测问题,提出了一种非等间距NGM(1,1,k)模型.为进一步提高模型的预测精度,利用线性插值方法对模型的背景值进行重构,以平均相对误差最小化为目标,建立了关于插值系数的优化模型,并运用穷举算法确定模型的最优插值系数.最后通过两个实例表明了非等间距NGM(1,1,k)模型及其优化模型的有效性和实用性.     

基于非线性规划的社会系统协调发展优化模型及其应用

社会系统协调发展优化问题是优化理论运用较少的领域.基于非线性规划方法,依据社会系统协调发展内涵,对社会系统协调发展目标函数做了具体改进,构建了社会系统协调发展优化模型,并根据协调发展类型分为超前型优化模型与滞后型优化模型.这两种优化模型的区别主要是约束条件的不同.依据优化模型的优化解,可以为某地区社会系统的协调发展提供清晰的调节与控制路径.最后利用上述方法对徐州地区物流基础系统、经济基础系统的协调发展进行了具体优化与调控.     

基于交叉验证的模型选择中投票和平均方法的对照

统计机器学习中评判模型好坏的准则,必须权衡模型的复杂度与训练数据的拟合优度.常用于评价模型好坏的方法包括AIC,BIC,Cp准则,交叉验证等.而在预测误差的估计中,使用最简单且最广泛的方法是交叉验证方法.考虑最广泛使用的标准K折交叉验证的两种选择方式:投票和平均.实验对照了模型选择中这两种不同的选择方式的差异,为将来人们在进行这两种方式的选择时作参考.     

柔性生产下马氏需求EPQ模型的最优(P,Q,T)策略

随机需求环境下研究柔性制造系统的经济生产批量(Economic Production Quantity,EPQ)模型的最优生产策略问题.在假设需求满足马尔可夫性且为需求状态依赖的条件下,根据顾客的需求程度对市场需求进行状态划分,建立了马氏需求模型,对系统未来的需求提出了科学的预测方法.在该模型的基础上,结合柔性制造系统的理论,构建了柔性生产下马氏需求EPQ模型.利用函数的凸性研究了新的EPQ模型的最优解的存在性,进而提出模型的最优(P,Q,T)策略.模型的数值算例表明,利用最优(P,Q,T)策略可以提高制造系统的生产率、缩短产品生产周期及实现小批量的生产模式.     

中国股票市场波动非对称性特征研究

利用三种 GARCH-M模型实证分析了中国股票市场不同发展阶段波动的非对称性特征 .结果发现 ,中国股票市场存在显著的波动非对称性 ,并且在不同阶段呈现不同特点 .对三种模型进行比较的结果显示 ,EGARCH-M模型是描述中国股市波动非对称性特征的最优模型 .     

数据包络分析中规模效率指数方法的一点注记

原有的规模效率指数方法是基于投入导向的模型进行分析的,有悖于规模收益的定义,应该基于产出导向的模型.为此,把基于投入导向的方法转化为基于产出导向的方法.首先,通过理论证明原方法所提出的判定原则也适用于基于产出导向的方法,但由于使用的模型不同,两种方法存在本质的区别.第二,证明了投入导向和产出导向的BCC模型是不等价的,这直接说明两个方法是不等价的.第三,通过实例表明两种方法将产生不同的计算结果,并通过理论分析了其根本原因.上述结论表明,两种方法在投影方式和计算结果等两方面存在差别.因而,开展规模收益分析应该采用基于产出导向的方法.     

多类型复发事件下变系数加性乘积比率回归模型

本文基于多类型复发事件数据,讨论了一个新的加性乘积比率回归模型,该模型包括两部分,其中第一部分为可加Aalen模型,其中协变量影响为加性的且与时间有关.第二部分为Cox回归模型,其中协变量有乘性影响.利用估计方程的方法,给出了该模型中未知参数和非参数函数的一种估计方法,并利用现代经验过程理沦证明了所得估计的相合性和渐近正态性.     

一类具有种内竞争率的竞争扩散模型稳态解的存在性和稳定性

利用Lyapunov-Schmidt约化方法和扰动理论,研究了一类带有不同种内竞争率的两物种竞争扩散模型.首先给出了半平凡稳态解的存在性和稳定性的充分条件,表明在一定条件下新物种可以入侵.在此基础上进一步证实了共存态的存在性和稳定性,研究表明在适当条件下相互竞争的两物种能够共存.所得结果改进和完善了已有的工作.     

交通流灰色RBF网络非线性组合预测方法

针对智能交通系统的开发,提出一种基于灰色GM(1,1)模型和RBF网络非线性组合的短时交通流预测方法.该方法采用三层结构的RBF网络将两种单一预测方法(灰色GM(1,1)模型和RBF网络)进行了非线性组合.利用实测数据对组合方法进行了仿真实验,结果表明:非线性组合模型的预测准确性高于单独的RBF网络预测的准确性;组合模型发挥了两种单一方法各自的优势,是短时交通流预测的有效方法.     

CVaR鲁棒均值-CVaR投资组合模型与求解

传统的均值-风险(包括方差、VaR、CVaR等)组合选择模型在计算最优投资组合时,常假定均值是已知的常值,但在实际资产配置中,收益的均值估计会有偏差,即存在着估计风险.在利用CVaR测度估计风险的基础上,研究了CVaR鲁棒均值-CVaR投资组合选择模型,给出了另外两种不同的求解方法,即对偶法和光滑优化方法,并探讨了它们的相关性质及特征,数值实验表明在求解大样本或者大规模投资组合选择问题上,对偶法和光滑优化方法在计算上是可行且有效的.     

金融中的Levy模型及其仿真

近20年来,金融中Levy模型与蒙特卡洛仿真技术日益受到重视. 在连续时间过程的金融建模中带跳跃的Levy模型相比于连续轨道的布朗运动模型能很好地刻画市场的跳跃,更好地拟合金融数据的统计特征,更准确地对衍生品定价. 但是,相较于经典的Black-Scholes模型,用Levy模型对衍生品定价以及求解对冲策略的计算复杂度大大增加. 蒙特卡洛仿真成为Levy模型计算中最重要的方法之一. 首先详细地介绍了Levy模型引入的背景,并引出仿真方法在其中重要的应用价值. 最后,简要地给出了Levy过程仿真及其梯度估计的基本方法.     

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??In the last few decades, longitudinal data was deeply research in statistics science and widely used in many field, such as finance, medical science, agriculture and so on. The characteristic of longitudinal data is that the values are independent from different samples but they are correlate from one sample. Many nonparametric estimation methods were applied into longitudinal data models with development of computer technology. Using Cholesky decomposition and Profile least squares estimation, we will propose a effective spline estimation method pointing at nonparametric model of longitudinal data with covariance matrix unknown in this paper. Finally, we point that the new proposed method is more superior than Naive spline estimation in the covariance matrix is unknown case by comparing the simulated results of one example.     

Dynamic mortality factor model with conditional heteroskedasticity

In most methods for modeling mortality rates, the idiosyncratic shocks are assumed to be homoskedastic. This study investigates the conditional heteroskedasticity of mortality in terms of statistical time series. We start from testing the conditional heteroskedasticity of the period effect in the naïve Lee-Carter model for some mortality data. Then we introduce the Generalized Dynamic Factor method and the multivariate BEKK GARCH model to describe mortality dynamics and the conditional heteroskedasticity of mortality. After specifying the number of static factors and dynamic factors by several variants of information criterion, we compare our model with other two models, namely, the Lee-Carter model and the state space model. Based on several error-based measures of performance, our results indicate that if the number of static factors and dynamic factors is properly determined, the method proposed dominates other methods. Finally, we use our method combined with Kalman filter to forecast the mortality rates of Iceland and period life expectancies of Denmark, Finland, Italy and Netherlands.     

R＆D和边干边学的混合模型的稳定性分析

本文研究了由R＆D和边千边学构成的混合模型的稳定性问题，利用二次Liapunov判别法．得出关于混合模型在确定和随机状态下的若干结论.     

Discriminant versus rough sets approach to vague data analysis

This paper presents a comparative study of the use of two different methods of data analysis on a common set of data. The first is a method based on rough sets theory and the second is the location model method from the field of discriminant analysis. To investigate the comparative performance of these methods, a set of real medical data has been used. The data considered are of both discrete and continuous character. During the comparison, particular attention is paid to data reduction and to the derivation of decision rules and classification functions from the reduced set.     

A Derivative Based Surrogate Model for Approximating and Optimizing the Output of an Expensive Computer Simulation

Approximation methods have found an increasing use in the optimization of complex engineering systems. The approximation method provides a 'surrogate' model which, once constructed, can be called instead of the original expensive model for the purposes of optimization. Sensitivity information on the response of interest may be cheaply available in many applications, for example, through a pertubation analysis in a finite element model or through the use of adjoint methods in CFD. This information is included here within the approximation and two strategies for optimization are described. The first involves simply resampling at the best predicted point, the second is based on an expected improvement approach. Further, the use of lower fidelity models together with approximation methods throughout the optimization process is finding increasing popularity. Some of these strategies are noted here and these are extended to include any information which may be available through sensitivities. Encouraging initial results are obtained.     

基于信息再利用的灰色系统GM(1.1)模型建模方法及应用

目的:寻找新的灰色系统GM(1.1)模型建模方法,建立拟合精度与预测精度较高的GM(1.1)模型.方法:在邓聚龙教授建模方法的基础上,用基于信息再利用的方法,建立新的灰色系统GM(1.1)模型.结果:用基于信息再利用的灰色系统GM(1.1)模型建模方法建立的GM(1.1)模型,其拟合精度与预测精度不但优于传统方法建立的GM(1.1)模型,而且优于其他改进方法建立的GM(1.1)模型.结论:基于信息再利用的灰色系统GM(1.1)模型建模方法不但建模过程简单适用,而且其建立的GM(1.1)模型拟合精度与预测精度优于其他改进方法建立的GM(1.1)模型,因而具有广泛的应用价值.     

Fractional-step <Emphasis Type="Italic">θ</Emphasis>-method for solving singularly perturbed problem in ecology

We consider a predator-prey model arising in ecology that describes a slow-fast dynamical system. The dynamics of the model is expressed by a system of nonlinear differential equations having different time scales. Designing numerical methods for solving problems exhibiting multiple time scales within a system, such as those considered in this paper, has always been a challenging task. To solve such complicated systems, we therefore use an efficient time-stepping algorithm based on fractional-step methods. To develop our algorithm, we first decouple the original system into fast and slow sub-systems, and then apply suitable sub-algorithms based on a class of θ-methods, to discretize each sub-system independently using different time-steps. Then the algorithm for the full problem is obtained by utilizing a higher-order product method by merging the sub-algorithms at each time-step. The nonlinear system resulting from the use of implicit schemes is solved by two different nonlinear solvers, namely, the Jacobian-free Newton-Krylov method and the well-known Anderson’s acceleration technique. The fractional-step θ-methods give us flexibility to use a variety of methods for each sub-system and they are able to preserve qualitative properties of the solution. We analyze these methods for stability and convergence. Several numerical results indicating the efficiency of the proposed method are presented. We also provide numerical results that confirm our theoretical investigations.     

Properties of estimators of baseline hazard functions in a semiparametric cure model

We consider a semiparametric cure model combining the Cox model with the logistic model. There are the two distinct methods for estimating the nonparametric baseline hazard function of the model; one is based on a pseudo partial likelihood and the other is to use an EM algorithm. In this paper, we discuss the consistency and the asymptotic normality of the estimators from the two methods. Then, we show that the estimator from the pseudo partial likelihood can be characterized by the (forward) Volterra integral equation, and the estimator from the EM algorithm by the Fredholm integral equation. These characterizations reveal differences in the properties between the estimators from the two methods. In addition, a simulation study is performed to numerically confirm the results in several finite samples.