分层抽样下定量特征敏感性随机化模型的比估计法

基于定量特征敏感性随机化模型中的Eichhorn and Hayre模型,运用分别比估计法和联合比估计法对其总体均值作估计,并对两种方法下的估计量进行效率比较.结果表明:若各层的样本量比较大,各层的比估计比较有效,则分别比估计法的精度要优于联合比估计法.     

Adaptive Optimal Allocation in Stratified Sampling Methods

In this paper, we propose a stratified sampling algorithm in which the random drawings made in the strata to compute the expectation of interest are also used to adaptively modify the proportion of further drawings in each stratum. These proportions converge to the optimal allocation in terms of variance reduction and our stratified estimator is asymptotically normal with asymptotic variance equal to the minimal one. Numerical experiments confirm the efficiency of our algorithm. For the pricing of arithmetic average Asian options in the Black and Scholes model, the variance is divided by a factor going from 1.1 to 50.4 (depending on the option type and the moneyness) in comparison with the standard allocation procedure, while the increase in computation time does not overcome 1%.     

数量特征敏感性问题随机化回答的改进模型

研究数量特征敏感性问题的调查方法,设计了一种数量特征敏感性问题的随机化回答改进模型,计算了改进模型的估计量及其方差,并对改进模型进行了分析,得出改进模型具有较好的精度。     

数量特征敏感性问题随机化回答的改进模型

研究数量特征敏感性问题的调查方法,设计了一种数量特征敏感性问题的随机化回答改进模型,计算了改进模型的估计量及其方差,并对改进模型进行了分析,得出改进模型具有较好的精度．     

多目标优化配气数学模型及其MATLAB编程实现

为了更全面地理解区块优化配气问题,更快速深入地掌握区块优化配气方法,将详尽的数学模型及其MATLAB代码同时给出.采用高次多项式拟合得到目标函数,根据产量、经济效益、混合目标三种需求,结合现场主要考虑的四种约束条件,建立了全面而详尽的三类数学模型.给出了求解模型最关键的四个MATLAB函数文件,并与其数学模型一一对应.求解得到了三类模型在不同约束状态下的配气方案,并与现场常用的简易配气方法进行比较,最后给出了一种充分利用各类设备处理能力的方法.     

双辅助信息随机化回答模型的两种估计量

随机化回答技术是进行敏感性问题调查和推算总体特征比例的一种有效方法.通过在随机化回答技术中使用辅助信息,可以提高估计量的精度,从而得到更为合理的调查结果.在随机化回答技术中引入两个辅助信息,提出比方法和比方法与回归方法组合的两种估计量,计算了这两种估计量的均方误差,并通过数值计算验证了两种估计量的合理性.     

Determination of Optimum Production Quantity for a Two-Stage Production System

In this paper, a method is given for determining the optimum production quantity for a two-stage production system. The method assumes that a lot size is manufactured through two production stages with only one set-up at each stage. A production lot is manufactured in a number of sub-batches of sizes which reduce the total manufacturing cycle time of a production lot. The production quantity is considered 'optimum' when the sum of all the costs is minimized.     

"物流配送中心选址的随机数学模型"的有效性研究

从实际应用出发，对文献[1]中提出物流配送中心选址的随机数学模型进行了有效性分析。通过计算机模拟得到的数据，分别应用随机数学模型和传统选址方法对不同规模的配送网络进行选址，通过分析两者在不同条件下的总费用，得到了该随机模型的一些有趣的性质，为实际中的应用提供了依据。     

一种可供选择的随机化调查方法

本文提出了一种新的随机化调查方法,Warner(1965)和Horvitz,Simmons(1967)随机化回答方法是本文方法的特殊情形.这一方法比Warner(1965)方法和Mangat(1990)方法更有效. 当考虑存在不真实回答时,在一定的条件下并且不损失对个体保护度和不增加调查成本的前提下,提出的方法优于Simmons(1967)方法和Sarjinder Singh(2000)方法.对简单随机不放回抽样,在总体非敏感属性个体比例未知的情况下,给出了两个子样本容量的最优配置.     

安全投资优化分配方法证明及应用

分析并证明了用 Cobb-Douglsa函数建立安全投资优化分配模型及确立安全优化分配比例系数的正确性 ,并将该方法用于工厂实际工作 ,取得了满意的结果 .文中提出的投资优化分配方法具有较高的实用价值和广泛的应用前景     

两阶段半监督加权朴素信念分类模型

针对目前半监督分类算法中未考虑缺失属性隐含信息和算法复杂度高的情况,改进了朴素信念分类,提出了两阶段半监督加权朴素信念分类模型。与直推支持向量机对比实验结果表明两阶段半监督加权朴素信念分类模型减少了分类时间,并且在其能够明确分类的样本上的正确率与直推支持向量机相当,是一种有效的不完整数据分类算法。     

基于可信性理论的两阶段生产计划模型

为了准确有效地处理农业生产中的不确定性因素,基于可信性理论和两阶段模糊优化方法提出一类新的带有最小风险准则的两阶段模糊农业生产计划模型.然后,讨论可信性函数的逼近方法并且设计一个基于逼近方法、神经网络和模拟退火的启发式算法来求解这个两阶段模糊农业生产计划最小风险模型.最后,给出一个数值例子来表明所设计算法的可行性和有效性.     

An Optimum Multivariate Stratified Sampling Design with Nonresponse: A Lexicographic Goal Programming Approach

In a multivariate stratified sample survey with L strata and p > 1 characteristics, defined on each unit of the population, let the estimation of all the p-population means be of interest. As discussed by Cochran (1977), since the optimum allocation for one characteristic will not in general be optimum for other characteristics some compromise must be reached in a multiple characteristics stratified surveys. Various authors worked out allocations that are based on a compromise criterion. The resulting allocations are optimal for all characteristics in some sense, for example an allocation that minimizes the trace of the variance-covariance matrix of the estimators of the population means or an allocation that minimizes the weighted average of the variances or an allocation that maximizes the total relative efficiency of the estimators as compared to the corresponding individual optimum allocations. In the present paper the problem of optimum allocation in multivariate stratified random sampling in the presence of nonresponse has been formulated as a multiobjective integer nonlinear programming problem and a solution procedure is developed using goal programming technique. Three numerical examples are worked out to illustrate the computational details. A comparison of the proposed method with some well known methods is also carried out to show the practical utility of the proposed method.     

Optimum Allocation of Processes in a Distributed Environment: A Process-to-Process Approach

The process allocation problem (PAP) concerned with the assignment of a number of communicating processes to a certain number (not known a priori) of identical processors in a telecommunications environment is examined. The objective is to minimize the total message-passing between processes residing on different processors subject to constraints on the processing power and storage capacity (code-, data-storage and occupancy) of the processors. Constraints imposed on the co-location of certain processes on the same processor are also included. The problem is formulated as a 0-1 linear maximization problem, taking into account only the number of processes involved, while the number of processors required is produced automatically with the optimum solution. An implicit enumeration algorithm is developed which produces an optimum message-passing allocation. Computational results of a set of random problems which have similar characteristics to a real-world application in telecommunications are also presented.     

一种调查敏感性问题的随机化方法

根据已有的调查定性敏感性问题的方法,本文提出了一种新的随机化调查方法,该方法得到的估计量在一定条件下具有较小的方差.     

A Two-Stage Group Testing Model for Infections with Window Periods

We present a two-stage group testing model for the detection of viruses in blood samples in the presence of random window periods. As usual, if a tested group is found to be positive, all its members are treated individually. The groups that were tested negative return for a second round after a certain time, new blood samples are taken and tested after pooling. The given system parameters are the size of the population to be screened, the incidence rates of the infections, the probability distributions of the lengths of the window periods, and the costs of group tests. The objective is to minimize the expected cost of running the system, which is composed of the cost of the conducted group tests and penalties on delayed test results and on misclassifications (noninfected persons declared to be positive and, more importantly, persons whose infections have not been identified). By an appropriate choice of the group size and the waiting time for the second round of testings one wants to optimize the various trade-offs involved. We derive in closed form all the probabilistic quantities occurring in the objective function and the constraints. Several numerical examples are given. The model is also extended to the case of several types of viruses with different window periods.     

GM(1,1)模型的理论缺陷及其基于时间响应函数的优化分析

分析了GM(1,1)预测模型存在的理论缺陷和禁区,指出在形成预测模型时规定X^(1)(1)为已知条件是不合理的,应根据实际情况选用其他数据.构建了基于时间响应函数的优化模型,按照变化系数阀值,界定了优化模型的有效区.经过数值模拟,发现优化的GM(1,1)模型优于传统GM(1,1)模型,因此,提出的新的优化模型,为提高GM(1,1)模型预测精度提供了新的途径.