双向分层抽样及广州市商品房空置率调查推断

1.双向分层抽样及抽样误差 (一) 双向分层抽样 双向分层抽样是先将全及总体中的所有单位按某两个主要标志分层,使每—个总体单位既属于一个方向上的主要标志的某一层,又属于另—个方向上另一个主要标志的某一层。然后根据实际需要,按不同的概率在这些交叉层(称为格)中采用整群不重复抽样方式方法,各抽取一定数目的调查单位构成所需要的样本。 通过对两个主要标志从两个方向上分层,可以从两个方向上把全及总体分成性质比较接近的类型组,使两个方向上组内标志值之间的差异缩小,同时,按随机原则在各层中抽取一定数目的样本单位,使样本单位的两个标志值的分布更接近全及总体的分布,从而可以在两个方向上提高样本的代表性,缩小抽样误差。 双向分层抽样的每一个样本单位带有两个标志的信息,可提高样本单位使用价值,缩小了样本容量。双向分层抽样,还可以利用其中一个标志值的样本分层情况,对另一个标志值某一层的样本进行检查,看其是否与实际情形相符,具有检查功能。     

设计效应在复杂样本设计中的应用研究——以我国住户调查为例

本文首先给出并分析了影响复杂抽样设计效率的要素构成,明确了设计效应在抽样误差量度方面所具有的优势;其次,针对设计中的诸多要素,建立不同要素组合设计效应模型和综合设计效应模型,构建了对复杂抽样设计进行有效分析的理论分析框架;最后,以我国住户调查为例,给出设计效应在我国复杂样本设计中的应用。     

一类排序问题的最优解

本文讨论了将多个零件分派给多台机床加工的一类排序问题,机床的工效可以不一样,但假设零件在不同机床上的加工时间成比例.给出了使总花费时间最小的计算方法,这是一种多项式算法.当零件在所有机床上的加工时间与在其中某一机床上的加工时间之比均 为正整数时,进一步给出一种更为简便的算法——标号法.     

固定样组纵向调查"间歇式"期单元无回答的加权调整

期单元无回答误差是固定样组纵向调查中经常出现的一类抽样误差，如果不对之进行调整，则往往造成估计量的偏差，已经提出的两种加权调整方法不易处理“间歇式”期单元无回答。在本文中，我们提出了纵横加权调整方法，这一方法克服了已有方法的不足，我们所作的模拟研究表明，纵横加权方法降低了估计量的偏差，并在作两调查期指标均值变化分析时，充分利用了两期回答状态的相关信息，提高了变化估计量的准确度。     

政府花费改变对经济影响的理论分析

本文采用无限期内生时间偏好率模型,研究了政府花费对经济的影响.指出长期的政府花费增加可以使资本存量水平上升,消费水平下降和债券持有量上升;同时,短期分析表明政府花费增加可以减少初始投资率,增加政府债务率水平;但短期的政府花费增加同样导致资本存量水平上升,消费水平下降和债务水平的增加.     

质量及可靠性——管理图的原理

<正> 对产品工程质量进行工序控制的方法之一,是应用管理图,边生产边抽查产品,根据一定的概率标准判别实际测得的抽样值是来自偶然的抽样误差,抑或来自确定原因的系统误差.抽样误差由公差或规格范围来解决,工序控制是针对系统误差的,一旦发现要及时校正生产过程,以保证往后生产过程的稳定.本文介绍管理图界限的数学基础.     

对称约束平衡Procrustes问题的一个简单解法

本文提出一个非常简单的方法,解决对称约束的平衡Procrustes问题:给定两个同样大小的矩阵A,B∈R~m×n,求对称正交阵Q,使‖AQ—B‖_F达到最小.该方法同时具有较好的数值稳定性.     

有序信息在空间立方体抽样中的拓展研究

在空间调查领域中,分布良好的样本中很少会同时出现彼此接近的单元.将地理坐标作为平衡变量的立方体法存在无法捕捉总体单元的空间依赖性的不足.文章在立方体法能有效捕获总体空间趋势的基础上,创新性地引入系统抽样的思想.文章提出的抽样设计方法在保持单元空间相对位置的同时,充分利用地理坐标信息,集中相似单元点获得有序总体,使原先总体具有“空间趋势”.首先,在保持空间相关性的前提下,将抽样单元排序问题转换为单源最短路径问题.然后,利用Dijkstra算法获得有序总体,在快速立方体法的飞行阶段更新初始包含概率进行有序空间抽样.最后,通过模拟研究和实证分析表明对空间总体按照相似单元彼此靠近的原则进行排序,可减少抽样误差,获得较为均衡的样本,从而验证所提抽样思路的可行性.     

基于最大离差和最大联合熵的多方案优选方法

针对多方案优选中指标权重确定问题,提出了基于最小离散和最大广义联合熵的组合赋权方法,建立了组合权系数优化模型。该方法一方面根据评价指标对方案决策所起作用大小赋予不同的权重;另一方面尽量能够消除组合赋权中的不稳定性,使各方法各指标权数赋予平衡因子后广义的联合熵最大,从而使得全局的不确定性最小。最后通过实例说明了此方法合理、稳健。     

数学竞赛申几类限定几何极值问题

已知直线l或圆O及两定点A、B，在其上求一点P，使PA＋PB为最小．此问题称为限定几何极值问题，本文对它拓广，并对由此衍生的竞赛题的背景进行探讨及给出新解法．一般可表述为：A、B为已知圆锥曲线M外的两定点，求M上任一点P到A、B距离之和的最值．1．当线段AB与曲线M有公共点P。时．（1）PA＋PB有最小值，最小值即为线段AB的长．（2）①若M是无界曲线，PA＋PB无最大值．②若M是有界且连续的曲线，当点P为以A、B为焦点的椭圆系与M的“最后”一个公共点（再扩大一点即把M内含）时，PA＋PB最大，最大值即为此时椭圆长轴的长．…     

A Procedure for Locating Emergency-Service Facilities for All Possible Response Distances

The problem of locating emergency-service facilities involves the assignment of a set of demand points to a set of facilities. One way to formulate the problem is to minimize the number of required facilities, given that the maximum distance between the demand points and their nearest facility does not exceed some specified value. We present a procedure for determining the numbers of such facilities for all possible values of the maximum distance. Computational results are presented for a microcomputer implementation.     

Computing Normalizing Constants for Finite Mixture Models via Incremental Mixture Importance Sampling (IMIS)

This article proposes a method for approximating integrated likelihoods in finite mixture models. We formulate the model in terms of the unobserved group memberships, z, and make them the variables of integration. The integral is then evaluated using importance sampling over the z. We propose an adaptive importance sampling function which is itself a mixture, with two types of component distributions, one concentrated and one diffuse. The more concentrated type of component serves the usual purpose of an importance sampling function, sampling mostly group assignments of high posterior probability. The less concentrated type of component allows for the importance sampling function to explore the space in a controlled way to find other, unvisited assignments with high posterior probability. Components are added adaptively, one at a time, to cover areas of high posterior probability not well covered by the current importance sampling function. The method is called incremental mixture importance sampling (IMIS).

IMIS is easy to implement and to monitor for convergence. It scales easily for higher dimensional mixture distributions when a conjugate prior is specified for the mixture parameters. The simulated values on which the estimate is based are independent, which allows for straightforward estimation of standard errors. The self-monitoring aspects of the method make it easier to adjust tuning parameters in the course of estimation than standard Markov chain Monte Carlo algorithms. With only small modifications to the code, one can use the method for a wide variety of mixture distributions of different dimensions. The method performed well in simulations and in mixture problems in astronomy and medical research.     

Economic optimization of off-line inspection with inspection errors

In this paper, we find the optimal inspection policy for the production process of a finite batch of items with inspection errors. We study the effects of inspection errors on the optimal solution and determine which unit should be inspected to minimize the expected total costs. We also find the expected number of inspections for a given batch size. Comparisons of total costs are investigated numerically among the cost minimizing policy, the policy of perfect information, and the policy of zero-defects.     

Supply chain design under uncertainty using sample average approximation and dual decomposition

We present a supply chain design problem modeled as a sequence of splitting and combining processes. We formulate the problem as a two-stage stochastic program. The first-stage decisions are strategic location decisions, whereas the second stage consists of operational decisions. The objective is to minimize the sum of investment costs and expected costs of operating the supply chain. In particular the model emphasizes the importance of operational flexibility when making strategic decisions. For that reason short-term uncertainty is considered as well as long-term uncertainty. The real-world case used to illustrate the model is from the Norwegian meat industry. We solve the problem by sample average approximation in combination with dual decomposition. Computational results are presented for different sample sizes and different levels of data aggregation in the second stage.     

Integrating transportation and production: an international study case

The problem we study is inspired by the real case of Mesdan S.p.A., an Italian company worldwide leader in the textile machinery sector, which has two production units with two warehouses, one located in Italy (Brescia) and the other in China (Foshan). The critical point in this logistic system is the integration between production and transportation management, given the long distance between Brescia and Foshan. Shipments are performed by the means of different types of vehicles with different unit costs and significantly different lead times. Variable production costs, variable and fixed transportation costs and, possibly, inventory costs are charged in the objective function. Different production policies are compared. Our aim is to determine integrated policies that minimize the total cost of the system. We formulate integer linear programming models for the solution of these problems, and we solve the real instance and carry out a sensitivity analysis of the optimal solution.     

Impact of measurement error on container inspection policies at port-of-entry

Containers arriving at a port-of-entry are inspected using sensors and devices to detect drugs, weapons, nuclear materials and other illegal cargo. Measurement errors associated with the inspection process may result in higher misclassification of containers. In this paper, we propose and formulate three inspection policies for containers at port-of-entry assuming the presence of sensor measurement errors. The optimization of the policies is carried out and the performance of each in terms of misclassification probabilities is compared. In each of the policies, the optimum settings are determined by minimizing the probability of false rejection while limiting the probability of false acceptance to a specified tolerance level. The results show that repeat inspections improve performance in terms of correct container classification. Expressions are presented for container misclassification in a single station, as well as in systems with several inspection stations arranged in different configurations such as series, parallel, series-parallel and parallel-series.     

On the construction of prefix-free and fix-free codes with specified codeword compositions

We investigate the construction of prefix-free and fix-free codes with specified codeword compositions. We present a polynomial time algorithm which constructs a fix-free code with the same codeword compositions as a given code for a special class of codes called distinct codes. We consider the construction of optimal fix-free codes which minimize the average codeword cost for general letter costs with uniform distribution of the codewords and present an approximation algorithm to find a near optimal fix-free code with a given constant cost.     

Solving the capacitated lot-sizing problem with backorder consideration

In this research, we formulate and solve a type of the capacitated lot-sizing problem. We present a general model for the lot-sizing problem with backorder options, that can take into consideration various types of production capacities such as regular time, overtime and subcontracting. The objective is to determine lot sizes that will minimize the sum of setup costs, holding cost, backorder cost, regular time production costs, and overtime production costs, subject to resource constraints. Most existing formulations for the problem consider the special case of the problem where a single source of production capacity is considered. However, allowing for the use of alternate capacities such as overtime is quite common in many manufacturing settings. Hence, we provide a formulation that includes consideration of multiple sources of production capacity. We develop a heuristic based on the special structure of fixed charge transportation problem. The performance of our algorithm is evaluated by comparing the heuristic solution value to lower bound value. Extensive computational results are presented.     

Hop Constrained Network Design Problem with Partial Survivability

This paper studies the hop constrained network design problem with partial survivability, namely, given an undirected network, a set of point-to-point demands (commodities), and transmission link costs, identify two node disjoint paths for each demand (commodity) to minimize the total costs subject to the constraints that each demand is routed and traverses at most a specified number of links (or hops) on both the paths.A mathematical programming formulation of the problem is presented and an efficient solution procedure based on the linear programming relaxation is developed. Extensive computational results across a number of networks are reported. These results indicate that the solution procedure is effective for a wide range of problem sizes.     

Optimal control of polling models for transportation applications

We formulate and analyze a dynamic scheduling problem for a class of transportation systems in a Markov Decision Process (MDP) framework. A transportation system is represented by a polling model consisting of a number of stations and a server with switch-over costs and constraints on its movement (the model we have analyzed is intended to emulate key features of an elevator system). Customers request service in order to be transported by the server from various arrival stations to a common destination station. The objective is to minimize a cost criterion that incorporates waiting costs at the arrival stations. Two versions of the basic problem are considered and structural properties of the optimal policy in each case are derived. It is shown that optimal scheduling policies are characterized by switching functions dependent on state information consisting of queue lengths formed at the arrival stations.