河北省城市经济发展水平的实证研究

以2016年河北省城市经济发展水平为研究对象,选取10个经济指标,利用因子分析法将城市经济发展水平评价指标压缩为2个公因子,以综合加权因子得分为变量进行聚类分析,结果表明河北省各城市间的经济发展存在较为严重的不平衡性,由此提出促进河北省城市经济水平均衡发展的决策建议.     

城市经济可持续发展的综合评价

将多指标可拓综合评价方法应用于城市经济可持续发展水平评价 .分析了城市经济可持续发展水平的影响因素 ,建立了城市经济可持续发展水平评价的物元模型 .将多指标城市经济可持续发展水平的目标评价归结为单目标决策 ,比较简明确切地反映出城市经济可持续发展水平 .     

湖北省农业现代化评价指标体系构建及评价研究

在分析农业现代化评价研究现状和进展的基础上,参考和借鉴国内外最新研究成果,提出湖北省农业现代化评价指标体系和评价方法.指标体系由农村社会发展水平、农业投入水平、农业产出水平和农业可持续发展水平4项主体指标和16项个体指标构成.同时,运用唯一参照物比较判断法(G_2-法)和Delphi法相结合确定指标权重,并在此基础上采用多指标综合测度法构建评价模型,对湖北省不同水平年的农业现代化建设水平及发展态势进行实证分析,探索提高湖北省农业现代化建设水平的对策建议,为湖北省农业现代化发展提供决策参考.     

基于多元统计分析的城市竞争力研究

应用多元统计分析方法对34个省会城市和计划单列市的竞争力进行了综合评估.首先设立城市竞争力评价指标,由于指标之间存在相关性,采用因子分析法选取公共因子作为评价的综合指标,然后构造评价函数,根据城市竞争力在各公因子上的得分及其综合排名,分析各城市的发展特色和竞争优劣点.基于因子分析法得到的公因子替代原始变量,分别采用系统聚类分析法和K均值聚类分析法给各城市划分类型,比较两种不同聚类法得出的结果,分析聚类结果可靠性.最后基于综合评估结果给出了提高城市竞争力的合理建议.     

江苏省区域经济发展水平分析

针对江苏各城市间经济发展水平的差异性,本文选用十个指标构建综合评价体系,利用SPSS软件进行因子分析和聚类分析.结果表明13个地区之间经济发展差异较为突出,主要受综合实力因子和农业因子影响.大体分为三类:经济发达城市、经济次发达城市、经济发展滞后城市.最后,文章提出缩小江苏区域经济发展差异的一些建议.     

因子分析法在科技期刊综合评价中的应用

以2009版中国期刊引证报告(扩刊版)10个指标数据为依托,采用因子分析法对21种物理学中文核心期刊进行综合评价,并与常见的总被引频次、影响因子评价法进行比较,以期为物理学期刊评价指标的选取和期刊综合水平的提高提供定量参考.结果表明,因子分析法全面考虑了影响期刊水平的各引用指标和期刊特征指标,不仅解决了各指标之间的相关重叠性,还客观确定了各指标权重,并较好地消除了期刊自引率过高引起的影响力评价失真问题,最终的综合评价结果与单指标评价相比更加客观和准确.     

基于灰色关联分析的河南各市可持续发展状况综合评价

构建合适的可持续发展综合指标体系,并获取河南17个城市2003年到2013年10年内的指标数据,运用灰色系统理论中的灰色关联分析对六个城市的可持续发展状况排序,然后以灰色系统为核心研究工具,运用灰色综合评价法对评价数据进行分析得出最佳的城市可持续发展指标体系的指标数据.最后提出了优化城市发展的若干建议.     

城市可持续发展综合评价方法及应用

建立了基于层次分析法的城市可持续发展综合评价方法 ,以城市可持续发展理想状态的各项指标值为参照 ,可对任一城市的可持续发展水平进行相对评估 .最后 ,以国外先进国家的平均发展水平为参照 ,对深圳市近年来可持续发展状况进行了综合评价与分析 .     

基于SEM的我国重要城市现代化水平综合评价模型研究

基于结构方程(SEM)理论,以2008年统计数据为样本对影响城市现代化的测量指标进行了一阶、二阶验证性因素分析,得到了结构方程模型.并以测量指标在因子上的负荷、路径系数为基础构建权重,建立了城市现代化水平综合评价模型.对我国重要城市(直辖市、主要省会城市)现代化水平程度进行了相对评价,得到较为满意的结果.     

基于熵值-双向激励的黑龙江城市经济发展的动态综合评价

针对黑龙江省2013-2017年十个主要城市的主要经济发展指标的样本数据,考虑到不同指标所包含信息量的不同,通过采用熵权法对原始数据进行处理,并对所得评价信息进行线性集结,得到了十个城市在每一年的静态综合评价值,然后在静态综合评价基础上引入激励因子,构建激励控制模型,并结合时间维度对黑龙江各城市经济发展实现了动态综合评价,可为政府有关部门制定相关决策和发展规划提供一定参考.     

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.     

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.     

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.     

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.     

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.