简约指标体系下我国省级信息化水平综合评价——基于相关分析和主成分分析方法

在海选省级信息化初始评价指标的基础上,采用相关分析和主成分分析定量筛选方法对初始指标体系进行变量筛选;保证筛选出的指标对评价结果有显著影响的前提下,构建了省级信息化评价简约指标体系;用22%的指标反映了99%的原始信息.最后综合各主成分构造的综合评价模型对我国31个省的信息化水平进行了实证分析,得到了较好的评价结果.     

关于主成分分析综合评价函数质疑的讨论

主成分分析综合评价是国内较流行的一种综合评价方法,这里对主成分分析综合评价函数的五个质疑或断言,用主成分分析模型、主成分分析综合评价的合理性条件进行了讨论,结果:主成分分析综合评价函数是果,主成分及其包含的信息是因,不能用它们包含的信息比较后推断,因果关联的它们既应分别深入分析,又应结合分析,效果会更好.并结合实例说明:具有合理性条件的主成分分析综合评价改进步骤,更具有合理性和有效性.     

对应用主成分法进行综合评价的探讨

本文针对用主成分法进行综合评价时存在的缺点,提出了分组主成分评价法。即先用因子分析法对变量进行分组后,然后再分别对各组变量进行主成分评价,既保证了主成分法的优点,也克服它在评价中的缺点,提高综合评价结果的合理性。并用该方法对实例进行了分析,取得了较好的效果。     

因子分析在京杭大运河水质评价中的应用

用主成分分析的方法找出影响京杭大运河淮安段水质的主因子,求出因子得分函数,计算各样本的因子得分及水质综合评分,据此对京杭大运河淮安段各监测断面的水质进行评价.     

我国各省(市、自治区)高技术产业发展水平因子分析与综合评价

建立了高技术产业发展水平的综合评价指标体系,采用主成分因子分析法进行了有效降维,得出了衡量高技术产业发展水平的四个公共因子,构建了相应的客观加权的综合评价模型.通过对31个省(市、自治区)的定量分析,发现了我国高技术产业在东西部发展不均衡,在东部地区尤其是珠江三角洲、长江三角洲、环渤海地区显示出明显优势的特点,这与我国实情吻合甚好.最后,根据因子得分分布图将31个省(市、自治区)分成了10类,分类提出了如何加速高技术产业发展的建议.     

产业集群创新能力评价模型的比较研究

针对产业集群创新能力评价的一些复杂方法,以文献中的基于BP神经网络的产业集群创新能力评价模型作为比较对象,提出了两种评价模型:组合评价模型和主成分指数模型.前者将变异系数法和Topsis法组合使用,用以评价产业集群创业能力;后者则是对所有参评样本的评价指标进行主成分分析,以主成分的方差贡献率为权重,构建主成分综合指数,从而形成产业集群创新能力的综合评价指数模型.对这两个模型用来自比较对象模型的同一数据进行了验证,三个模型都得出了非常相近的结果,而这两种模型更具可操作性且易于解释,这两者相比,主成分分析的方法则更为简单易行.     

基于主成分分析法的综合评价方法的改进

针对用主成分分析法做综合评价存在的问题,提出了改进的方法即当第一主成分的综合评价值和熵值法得到的综合评价值具有一致性时,将两种评价结果进行集成综合评价,若两种评价结果不具有一致性时则采用主成分聚类法进行综合评价.     

学生成绩排名的综合评价模型

对学生成绩排名提出几种新的综合评价模型:主成分分析模型,专家调查法-主成分分析模型,因子分析模型.然后利用一般使用模型和上面介绍的几种模型对理学院信计2000级学生成绩进行分析处理.最后对各种模型比较,找出一种较科学、公平、合理的评价模型.     

广东省经济发展状况的统计分析

选取广东省21个地级以上市为样本,对广东省经济发展现状进行统计分析.选取影响经济发展的主要指标,并进行分析认证.利用主成分分析法得出影响经济发展的两个主要成分"综合经济发展实力"和"社会富裕程度",根据这两个主成分对21个市的经济发展状况进行排序和综合评价.最后运用聚类分析法将广东省21个市分为三个区域.通过全方位地分析广东省各市经济社会的发展水平及差距,为广东省各市实现均衡发展提供一些理论依据和合理化建议.     

湖南省各市州经济发展水平评价

本文利用主成分分析法分析了湖南各市州的经济发展水平和经济综合实力,对湖南省14个市州的经济发展情况做出综合评价,得出了各市州在湖南省的经济地位.最后用系统聚类分析法进行了验证,结果表明用多元统计分析法来评价地区经济发展水平是可行的.     

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     

统计方法在研究微积分与后续课程相关性中的应用与实证分析

着眼于本科生后期专业课程的学习效果,探讨微积分课程学习效果的有效性.以北京理工大学某经管类专业全部学生大一大二两学年的学习成绩为依据,分析微积分课程学习与后续理科课程学习的相关性,提出评估微积分课程学习效果的量化指标θ值的概念,并基于回归分析和相关性分析给出其算法.在实例分析中,通过研究,发现学生微积分课程学习效果的好坏会影响他们对后续相关课程的学习,同时也发现学生将微积分知识运用到间接相关科目的能力比运用到直接相关的科目的能力薄弱.     

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.     

What drives value creation in investment projects? An application of sensitivity analysis to project finance transactions

Evaluating the economic attractiveness of large projects often requires the development of large and complex financial models. Model complexity can prevent management from obtaining crucial information, with the risk of a suboptimal exploitation of the modelling efforts. We propose a methodology based on the so-called “differential importance measure (D)$(D)$” to enhance the managerial insights obtained from financial models. We illustrate our methodology by applying it to a project finance case study. We show that the additivity property of D grants analysts and managers full flexibility in combining parameters into any group and at the desired aggregation level. We analyze investment criteria related to both the investors’s and lenders’ perspectives. Results indicate that exogenous factors affect investors (sponsors and lenders) in different ways, whether exogenous variables are considered individually or by groups.     

A sensitivity analysis algorithm for hierarchical decision models

In this paper, a comprehensive algorithm is developed to analyze the sensitivity of hierarchical decision models (HDM), including the analytic hierarchy process and its variants, to single and multiple changes in the local contribution matrices at any level of the decision hierarchy. The algorithm is applicable to all HDM that use an additive function to derive the overall contribution vector. It is independent of pairwise comparison scales, judgment quantification techniques and group opinion combining methods. The allowable range/region of perturbations, contribution tolerance, operating point sensitivity coefficient, total sensitivity coefficient and the most critical decision element at a certain level are identified in the HDM SA algorithm. An example is given to demonstrate the application of the algorithm and show that HDM SA can reveal information more significant and useful than simply knowing the rank order of the decision alternatives.     

我国工业污染分布状况研究

环境污染越来越受到我国的重视,而工业污染是造成环境污染的重要原因.为了对我国的环境污染分布状况进行深入了解,从总量出发,利用聚类分析和因子分析法分析了我国工业污染的分布情况,并探讨了各类地区工业污染差异的原因,为我国进行侧重点治理环境污染提出可行性建议.     

Composite multilinearity,epistemic uncertainty and risk achievement worth

Risk achievement worth is one of the most widely utilized importance measures. RAW is defined as the ratio of the risk metric value attained when a component has failed over the base case value of the risk metric. Traditionally, both the numerator and denominator are point estimates. Relevant literature has shown that inclusion of epistemic uncertainty (i) induces notable variability in the point estimate ranking and (ii) causes the expected value of the risk metric to differ from its nominal value. We investigate the conditions under which the equality of the nominal and expected values of a reliability risk metric holds. We then study how the presence of epistemic uncertainty affects RAW and the associated ranking. We propose an extension of RAW (called ERAW) which allows one to obtain a ranking robust to epistemic uncertainty. We discuss the properties of ERAW and the conditions under which it coincides with RAW. We apply our findings to a probabilistic risk assessment model developed for the safety analysis of NASA lunar space missions.     

When does data envelopment analysis outperform a naïve efficiency measurement model?

This paper compares the results from data envelopment analysis (DEA) to a naïve efficiency measurement model, which generates a scalar efficiency score by averaging all output–input ratios. Random data and real-life data are used to test the relative performance of the naïve model against various DEA models. The results suggest that the proposed the naïve model replicates DEA efficiency scores almost perfectly for constant return-to-scales and low heterogeneity in output–input data. It is therefore concluded that heterogeneity in output–input data is important to take advantage of the capability of DEA. It is also shown that heterogeneity is more relevant to efficiency measurement than the number of dimensions.     

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.