基于DEA模型的中国区域能源与环境效率评估

数据包络分析已经被广泛应用于研究中国各省市的能源与环境效率.在以前研究文献中,模拟弱可处置性建模时,隐含的假设所有决策单元采用统一均匀的减少系数.但是在实际生产过程中,面对边际减排成本最低的决策单元,减少非期望产出,也通常会带来成本效益,而且决策者也往往更倾向于减少那些带来成本效益更低的决策单元.因此,为解决这一实际问题,引进不同决策单元的非期望产出采用非均匀比例减少的基础上,考虑非能源投入和能源投入的划分,来构建评估中国各个省市能源和环境效率的生产可能集,然后利用改善的DEA模型计算2016年中国其中30个省市的能源与环境效率.结果表明,我国不同区域,相同区域不同省市之间的能源与环境效率都存在着较大差异.     

基于DEA-BCC模型的南通公交系统绩效评估

公交线路的量化绩效评估对提高公交运营效率、改善运营服务水平具有重要意义.以南通45条常规运营线路作为各决策单元,采用因子分析和主成分分析法,消除投入产出指标中的信息重叠因素后,应用DEA方法建立面向产出(BCC)的数学规划模型,计算比较决策单元之间的相对效率,对南通市公交系统作出客观绩效评价.     

基于F-DEA赋权耦合的军工企业装备维修能力评价模型

权重系数的确定是多指标综合评价中的核心问题.针对传统数据包络分析评价模型中对指标赋权方法研究的不足,以军工企业装备维修能力的评价为例,在构建装备维修能力评价指标的基础上,利用SPSS软件计算出各决策单元的因子得分,并依据可测函数的有关性质对非正得分进行转换,利用转换结果构建了数据包络分析的有关输入、输出矩阵,最终构建出基于因子分析（Factor Analysis, FA）与数据包络分析（Data Envelopment Analysis,DEA）的F-DEA赋权耦合模型.借助MATLAB 的运算结果,证明了该模型的可行性与有效性;并据此给出提高相应决策单元装备维修能力的关键因素及其改进方案.     

考虑环境因素的中国城市经济效率评价研究

在考虑环境因素情形下,实证考察中国城市经济效率水平.引入环境因素,构建中国城市经济效率评价指标体系;运用DEA-BCC模型对中国31个省会城市和计划单列市的经济效率进行评价,计算得出各决策单元的DEA评价值,并进行效率分析、投入冗余产出不足分析和区域比较分析.考虑环境因素情形下,中国城市经济效率总体上较好,综合效率、纯技术效率和规模效率平均值分别达0.912、0.855和0.940,但个体差异较大,存在个体间的非均衡发展;从规模报酬来看,太原、福州、南昌等10个决策单元的经济规模应适当增加;长春、哈尔滨、南京等9个决策单元的经济规模应适当减少;太原、长春、哈尔滨等16个决策单元存在不同程度的投入冗余和产出不足,亟待改进;中国城市经济效率存在区域间的非均衡发展,综合效率平均值呈现东部地区最高,中部地区次之,西部地区最低的格局.     

基于数据包络分析的并行生产系统效率评价方法

数据包络分析(DEA)是一种评价具有多投入、多产出决策单元的相对效率的线性规划方法.在现实世界中,决策单元有时呈现出由多个独立子系统构成的复杂并联网络系统,各子系统的投入/产出之和构成了系统的总投入/产出.目前,用于评价这种具有并联网络生产系统相对效率的模型主要有三种:网络DEA模型、多部门DEA模型和关联DEA模型.现有这些模型的基本特性和相互关系存在着不足,即子系统的效率分解和优化指数不唯一.为解决这一问题,提出了改进的并联DEA模型,并采用加拿大银行系统实例来说明所提出模型的合理性和有效性.     

模糊数据包络分析方法有效性分析

传统数据包络分析(DEA)模型只能用来评价具有精确投入和产出数据的决策单元.然而在实践中决策单元的投入产出数据可能存在一定模糊性.为了评价具有模糊投入产出数据的决策问题,研究工作者提出了模糊数据包络分析模型,并给出了相应的有效性定义.对于不同研究者提出的有效性定义方式有众多地方需要改进.通过这些改进提出了相关模型及新的有效性定义方式,并给出了相关实例.     

应用型本科高校数学与应用数学专业建设的探索与实践

分析了应用型本科高校数学与应用数学专业建设的现状,从应用型人才培养方案的构建、模块化教学体系的优化、应用型师资队伍的建设、学生应用能力的培养、实践教学的改革等五个方面探讨了应用型本科高校数学与应用数学专业教育教学改革的方法,提出了明确应用型培养目标、优化模块化教学体系和创新培养模式的专业建设思路.     

基于三阶段DEA的高校教育投入产出效率评价

采用三阶段DEA模型,选取辽宁省36所地方高校为研究对象,以2014年的统计数据为基础,对教育投入产出效率值进行测算.研究表明,剥离环境因素和随机干扰的影响后,教育投入产出效率由0.628下降到0.536,规模效率由0.802下降到0.637,而纯技术效率由0.779提升到0.833,其中33所高校的规模效率下降,31所高校的纯技术效率上升.从调整的数据可以看出,制约综合技术效率提升的主要因素已由调整前的纯技术效率变为调整后的规模效率.最后针对教育投入产出效率值分析,提出提升高校教育投入产出效率的对策.     

基于SMAA和公共权重的决策单元效率评价与排序

在数据包络分析(DEA)中,公共权重模型是决策单元效率评价与排序的常用方法之一。与传统DEA模型相比,公共权重模型用一组公共的投入产出权重评价所有决策单元,评价结果往往更具有区分度且更为客观。本文考虑决策单元对排序位置的满意程度,提出了基于最大化最小满意度和最大化平均满意度两类新的公共权重模型。首先,基于随机多准则可接受度分析(SMAA)方法,计算出每个决策单元处于各个排名位置的可接受度;然后,通过逆权重空间分析,分别求得使最小满意度和平均满意度最大化的一组公共权重;最后,利用所求的公共权重,计算各决策单元的效率值及相应的排序。算例分析验证了本文提出的基于SMAA的公共权重模型用于决策单元效率评价与排序的可行性。     

基于交叉评价策略的DEA灰色关联排序模型

数据包络分析(DEA)是评价决策单元相对效率的有效方法,其中的交叉效率评价方法可用来对决策单元进行区分排序.针对原有模型中交叉效率值的不唯一问题,结合灰色关联分析思想,构建理想决策单元,定义各决策单元与理想决策单元的灰色关联度,以灰色关联度值最大为目标,建立优化模型来计算输入和输出指标的最佳权重,据此得出决策单元的交叉效率值,实现对决策单元的完全排序.最后通过算例来验证模型的有效性和实用性.     

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.