山西省农民家庭消费水平和结构分析

本文利用统计计量学方法对山西省农民家庭消费水平和消费结构的变化及原因进行了实证分析。建立了山西省农民人均消费的消费函数和山西省农民总消费支出与食品、衣着、住房、日用品、燃料及文化生活服务支出的关系模型，并通过模型分析解释了农民的消费结构，也为决策者提供一些有效的参考资料。     

成分数据主成分分析及其应用

本文分析了传统主成分分析在成分数据分析中的不适应性，介绍了艾奇逊的中心化对数比变换和成分数据主成分分析，并以农民消费结构为例，重点讨论了成分数据主成分分析的实施步骤     

多维视角家庭碳排放驱动因素及脱钩效应分析——以陕西省为例

为科学合理安排陕西省城乡居民家庭减排规划,引导居民实施低碳消费,实现低碳发展.利用LMDI因子分解和Tapio指数法分别研究城乡家庭直接与间接碳排放驱动因素和脱钩分析.结果表明:①城乡居民家庭碳排放与居民消费支出总脱钩逐渐趋于弱脱钩关系;②家庭人均收入引起的城乡居民家庭直接、间接碳排放量与居民消费支出是“增长连结→扩张负脱钩”;家庭规模引起的城乡居民家庭直接、间接碳排放变化量与居民消费支出是“弱脱钩→强脱钩”;家庭户数引起的城乡居民家庭直接、间接碳排放变化量与居民消费支出是“强脱钩→弱脱钩→增长连结→扩张负脱钩关系”;③除农村家庭直接碳排放变化量外,能源消费强度和家庭消费率引起的城乡居民家庭直接、间接碳排放量与居民消费支出都是“弱脱钩→强脱钩”,能源消费结构和家庭消费结构引起的城乡居民家庭直接、间接碳排放量与居民消费支出都是从较理想的脱钩状态发展为扩张负脱钩关系.     

我国汽车消费贷款的前景预测与分析

本通过对我国居民五十年来耐用品消费结构变化的分析、我国未来汽车市场需求量的计算，以及我国汽车消费贷款的实例分析，从而得出我国未来汽车市场潜力很大，汽车消费贷款前景十分广阔，将为我国国民经济的发展，特别是汽车工业的发展起到理想的催化作用。     

基于扩展线性支出系统(ELES)的浙江农民消费需求变化分析

对先进的经济计量方法—扩展的线性支出系统进行解析.运用该模型及2002年—2006年浙江农民收入和消费数据,对浙江农民的消费结构、消费倾向和消费需求弹性进行分析.在此基础上提出相应建议.     

扩展线性支出系统在山西城镇居民消费结构分析中的应用

近年来，随着收入水平的不断提高，山西省城镇居民的消费结构发用扩展线性支出系统对消费结构做了系统的分析。     

环境治理政策的减排、增效与结构优化效果动态评价

为测度环境治理政策波动对产出、减排、要素配置、消费与产业结构的影响及其传导机制,将异质性消费偏好、产出差异性、环境效用和环境损失函数引入E-DSGE模型。政策强度分析发现,技术进步、排污税和消费税政策效果较强,环境控制和治污支出冲击效果较弱。政策效应分析发现,技术进步政策效应最优,能实现增产和减排双重目标,促进要素配置和居民消费、产出与消费结构改进;排污税和政府治污支出政策次优,能实现增产和减排的双赢目标,促进要素供给和产出结构清洁化,不利于消费提升和消费结构优化;环境控制与消费税政策效果最差,以牺牲产出实现减排,不利于要素配置,产出与消费结构改进效果较弱,消费税会抑制居民消费,但消费税政策效果随环境友好型家庭比例提升而加强。     

基于组合赋权法的我国CO2排放量影响因素的评价研究

为客观和准确地挖掘和评价我国CO₂排放量影响因素,选取技术投入、对外贸易开放程度、产业结构、能源消费结构、经济增长水平、人口规模和绿色植被用地面积等因素作为评价依据,构建我国CO₂排放量影响因素指标体系。在此基础上,基于组合赋权法构建我国CO₂排放量影响因素评价模型,实证分析2000~2011年我国CO₂排放量影响因素。组合赋权法结果显示:技术投入、产业结构、能源消费结构、经济增长和绿色植被用地面积是影响我国CO₂排放量的主要因素。组合赋权法在我国CO₂排放量影响因素评价分析的运用,提高了评价的客观性和科学性,为进一步确定CO₂排放量影响因素提供有价值的参考。     

中国“九五”期间城乡居民消费结构及变化趋势的比较分析

本文运用扩展的线性支出系统模型研究了我国“九五”期间城乡居民的消费结构状况。对城乡居民的消费结构和变化趋势进行了比较 ,同时给出了消费的合理性政策建议。     

多元统计在山东农村居民消费结构分析中的应用

本运用多元统计的因子分析、聚类分析法，对山东各地区农村居民的消费结构进行统计分析；然后在将山东省农村各地区分为四类的基础上，分析比较各类地区消费结构的差异及其成因；最后就如何促进山东农村居民合理消费，以此拉动山东农村经济的发展，提出相应的建议．     

初中学生心理测量的统计分析

本文用相关分析说明了〈中学生素质特点分类培养〉项目研究中所用各种心理诊断量表整体组合运用的合理性 ;用因子分析简化了测试项目的指标体系 ,选定了适当的公共主因子 ,并对公因子给予了合理的解释 ;用聚类分析依据因子得分对学生实施心理素质特点分类 ;根据学生的心理素质特点类型提出了相应的宏观培养策略     

Enabling e-transactions with multi-attribute preference models

This paper describes potential applications of multi-attribute preference models (MAPM) in e-commerce and offers some guidelines for their implementation. MAPM are methodologies for modeling complex preferences that depend on more than one attribute or criterion, and include multi-attribute utility theory, conjoint analysis, and the Analytic Hierarchy Process. There are numerous examples of applications in e-commerce that would benefit from the acquisition of information regarding the preferences of a consumer, a customer, an advice seeker, or a decision maker. Here, the focus is on applications of MAPM models in B2C and B2B websites, where preferences of consumers are assessed for the purpose of identifying products or services that closely match their needs.     

Singular Value Decomposition and Its Visualization

Singular value decomposition (SVD) is a useful tool in functional data analysis (FDA). Compared to principal component analysis (PCA), SVD is more fundamental, because SVD simultaneously provides the PCAs in both row and column spaces. We compare SVD and PCA from the FDA view point, and extend the usual SVD to variations by considering different centerings. A generalized scree plot is proposed to select an appropriate centering in practice. Several useful matrix views of the SVD components are introduced to explore different features in data, including SVD surface plots, image plots, curve movies, and rotation movies. These methods visualize both column and row information of a two-way matrix simultaneously, relate the matrix to relevant curves, show local variations, and highlight interactions between columns and rows. Several toy examples are designed to compare the different variations of SVD, and real data examples are used to illustrate the usefulness of the visualization methods.     

Actor analysis methods and their use for public policy analysts

Public policy analysts use methods rooted in OR and systems analysis to support policy makers in their judgement. In doing so, most policy analysts recognize the value of a certain understanding of the role of actors in policy making processes. Different methods are available to aid such understanding and, although they all focus on actors, there are important differences between them. Insight into the range of available methods and their characteristics will thus help policy analysts to learn more about the potential and limitations involved in analyzing multi-actor processes. This article provides such an overview, based on the main requirements these methods should meet. This overview is used to discuss some of the implications for policy analysts who are interested in analyzing multi-actor processes, focusing specifically on trade-offs between analytic quality and practical usability.     

Contribution Biplots

To interpret the biplot, it is necessary to know which points—usually variables—are the ones that are important contributors to the solution, especially when there are many variables involved. This information can be calculated separately as part of the biplot's numerical results, but this means that a table has to be consulted along with the graphical display. We propose a new scaling of the display, called the contribution biplot, which incorporates this diagnostic information directly into the display itself, showing visually the important contributors and thus facilitating the biplot interpretation and often simplifying the graphical representation considerably. The contribution biplot can be applied to a wide variety of analyses, such as correspondence analysis, principal component analysis, log-ratio analysis, and various forms of discriminant analysis, and, in fact, to any method based on dimension reduction through the singular value decomposition. In the contribution biplot, one set of points, usually the rows of a data matrix, optimally represents the spatial positions of the cases or sample units, according to an appropriate distance measure. The other set of points, usually the columns of the data matrix, is represented by vectors that are related to their contributions to the low-dimensional solution. A fringe benefit is that often only one common scale for the row and column points is needed on the principal axes, thus avoiding the problem of enlarging or contracting the scale of one set of points to make the biplot legible. Furthermore, the contribution biplot also solves the problem in correspondence analysis and log-ratio analysis of low-frequency categories that are located on the periphery of the map, giving the false impression that they are important, when they are in fact contributing minimally to the solution. This article has supplementary materials online.     

Matlab软件在多元统计分析中的应用

许多实际问题往往需要对数据进行统计分析,建立合适的统计模型.过去一般采用SAS、SPSS软件分析,本文给出Matlab软件在多元统计分析上的应用,主要介绍Matlab在主成份分析、聚类分析、判别分析上的应用。文中均给以实例,结果令人满意。     

Modelling,making inferences and making decisions: The roles of sensitivity analysis

Sensitivity analysis, robustness studies and uncertainty analyses are key stages in the modelling, inference and evaluation used in operational research, decision analytic and risk management studies. However, sensitivity methods -or others so similar technically that they are difficult to distinguish from sensitivity methods- are used in many different circumstances for many different purposes; and the manner of their use in one context may be inappropriate in another. Thus in this paper, I categorise and explore the use of sensitivity analysis and its parallels, and in doing so I hope to provide a guide and typology to a large growing literature.     

Implementing stochastic multicriteria acceptability analysis

Stochastic multicriteria acceptability analysis (SMAA) is a family of methods for aiding multicriteria group decision making in problems with inaccurate, uncertain, or missing information. These methods are based on exploring the weight space in order to describe the preferences that make each alternative the most preferred one, or that would give a certain rank for a specific alternative. The main results of the analysis are rank acceptability indices, central weight vectors and confidence factors for different alternatives. The rank acceptability indices describe the variety of different preferences resulting in a certain rank for an alternative, the central weight vectors represent the typical preferences favouring each alternative, and the confidence factors measure whether the criteria measurements are sufficiently accurate for making an informed decision.     

多元统计分析在大学生综合素质评价中的应用

德育和智育是衡量大学生综合素质的重要因素,本文根据天津工业大学某年度某班级学生的各科成绩和影响学生综合素质的相关因素的实际数据,应用因子分析对影响学生综合素质的各因素进行主成份分析,计算各个学生的因子综合得分并按得分高低进行排序,把它和常见的的两种评价方法进行比较,结果发现该方法能够弥补仅仅依靠平均积点分和按综合测评总分排序的不足。最后,以因子综合得分和平均积点分和综合测评总分为指标采用聚类分析对所有学生进行分类,得出了令人满意的结果。实证分析结果表明因子分析和聚类分析是衡量学生综合素质行之有效的方法。     

Post-objective determination of weights of the evaluation factors in public procurement tenders

In public procurement tenders the awarding criterion of the most economically advantageous bid employs weights to aggregate the numerical scores assigned to each proposal with respect to different evaluation factors. Typically these weights are fixed and subjectively set in advance. Methods, which objectively determine the weights after the opening of the sealed bids on the basis of the most or least favorable weights for each proposal, are developed. Post-objective methods of weight determination are shown to enhance the integrity of the evaluation process and to limit corruption in a public tender. The connection of Data Envelopment Analysis, which has been extensively applied to measure supplier efficiency, with the developed methods, is explored. Average least and most favorable weights are derived and optimal bidding strategies in this setting are presented.