中国转型期现代物流发展与制造业竞争力提升关系研究——基于2000—2007年分省面板数据的实证分析

通过运用数据包络分析(Data Envelopment Analysis),根据中国2000-2007年的职工人数、固定资产等经济数据构建一个能反映各地区制造业综合竞争力的单一数字指标,然后利用这些效率指标以及现代物流行业的外延数据构成面板数据(Panel Data)建立模型。通过实证分析得出:中国现代物流的发展有利于制造业竞争力提升。最后根据上述结论提出一些建议:大力发展面向生产的现代物流行业,细化深化专业分工,鼓励生产制企业改造现有业务流程,推进物流业务外包,加强核心竞争力和对外开放水平。     

基于DEA的交通运输与经济发展的协调性评价

数据包络分析法(DEA)是利用多个输入指标和多个输出指标的数据,对具有可比性的一些"单位"或"部门"(称为"决策单元")进行相对效率评价,应用于对交通运输和经济发展的协调性的动态综合评价,实际是对两个系统相互支持和利用的相对有效性进行动态评价,其评价结果可以通过计量回归模型来验证,即应用Chow's断点(Breakpoint)检验,从交通运输与经济发展计量模型结构发生显著变化的角度,可以进一步验证交通运输与经济发展之间协调性的动态变化趋势.     

供应链合作伙伴的分类及评价研究

文章在提出供应链合作伙伴二阶段模型的基础上,将多元统计方法中的聚类分析和系统工程中的密切值法综合应用于供应链合作伙伴第一阶段选择中：通过聚类方法将潜在的合作伙伴划分群体,再应用密切值法对聚类分析所得到的群体进行优先次序的排序和等级的划分,得出优先群体,供核心企业参考,以提高企业选择合作伙伴的效率并减少其选择成本。     

PCI技术在探地雷达系统中的应用

首先介绍探地雷达工作原理和PCI总线技术,然后通过WinDriver开发工具来快速实现对探地雷达信号的数据采集,并给出了PCI设备驱动程序的开发实例。     

Shape analysis for automated sulcal classification and parcellation of MRI data

We describe geometric invariants that characterize the shape of curves and surfaces in 3D space: curvature, Gauss integrals and moments. We apply these invariants to neuroimaging data to determine if they have application for automatically classifying and parcellating cortical data. The curves of sulci and gyri on the cortical surface can be obtained by reconstructing cortical surface representations of the human brain from magnetic resonance imaging (MRI) data. We reconstructed gray matter surfaces for 15 subjects, traced 10 sulcal curves on each surface and computed geometric invariants for each curve. These geometric features were used classify the curves into sulcal and hemispheric classes. The best classification results were obtained when moment-based features were computed on the sulcal curves in native space. Gauss integral measures showed that they were useful for differentiating the hemispheric location of a single sulcus. These promising results may indicate that moment invariants are useful for characterizing shape on a global scale. Gauss integral invariants are potentially useful measures for characterizing cortical shape on a local, rather than global scale. Gauss integrals have found biological significance in characterizing proteins so it is worthwhile to consider their possible application to neuroscientific data.     

Avoiding 'data snooping' in multilevel and mixed effects models

Summary.  Multilevel or mixed effects models are commonly applied to hierarchical data. The level 2 residuals, which are otherwise known as random effects, are often of both substantive and diagnostic interest. Substantively, they are frequently used for institutional comparisons or rankings. Diagnostically, they are used to assess the model assumptions at the group level. Inference on the level 2 residuals, however, typically does not account for 'data snooping', i.e. for the harmful effects of carrying out a multitude of hypothesis tests at the same time. We provide a very general framework that encompasses both of the following inference problems: inference on the 'absolute' level 2 residuals to determine which are significantly different from 0, and inference on any prespecified number of pairwise comparisons. Thus, the user has the choice of testing the comparisons of interest. As our methods are flexible with respect to the estimation method that is invoked, the user may choose the desired estimation method accordingly. We demonstrate the methods with the London education authority data, the wafer data and the National Educational Longitudinal Study data.     

Participant engagement in environmentally focused social network research

Environmentally focused social network analysis (Env. SNA) has increasingly benefited from engagement, which refers to the process of incorporating the individuals, organizations, actors, stakeholders or other study participants into the research process. Research about engagement in the wider field of environmental management shows that successful engagement often requires significant planning and exchange among researchers and stakeholders. While there is no one size fits all approach, several important guiding principles have been established. To date, this engagement literature has not focused specifically on SNA, even though several examples of engaged SNA exist in the literature and point to some specific challenges to engagement when working with relational data. Drawing upon data from a survey of researchers who have incorporated engagement into Env. SNA, we focus specifically on the goals, scope, effectiveness, benefits and challenges of doing engaged Env. SNA research. We additionally highlight four case studies and demonstrate that researchers and participants find engagement to be a valuable experience with benefits in the researchers’ understanding of the context and meaning of their findings. Finally, we provide recommendations to scholars looking to embark on engaged Env. SNA research.     

多源高维数据的多分类纵向整合分析及应用

多分类数据分析在实证研究中具有重要意义。然而,由于高维数、小样本及低信噪比等原因,现有的多分类方法仍面临信息量不足而导致的效果不佳问题。为此,学者们通过收集更多信息源 数据以更全面地刻画实际问题。不同于收集相同自变量的不同源样本,目前较为流行的多源数据收集了相同样本的不同源自变量,它们的独立性和相关性为统计建模带来了新的挑战。本文提出基于典型变量回归的多分类纵向整合分析方法,其中利用惩罚技术实现变量选择,并独特地考虑不同源数据间的关联结构,提出高效的ADMM算法进行模型优化。数值模拟结果表明,该方法在变量选择和分类预测 上均具有优越性。基于我国上证50的多源股票数据,利用该方法对2019年股票日收益率的影响因素进行了实证探究。研究表明,本文提出的多分类整合分析在筛选出具有解释意义变量的同时具有更好的预测效果。     

基于变系数模型的高维数据异同性识别方法研究

随着信息技术的发展,高维数据日益丰富。现实中,很多高维数据由多个主体各异的数据集融合而成。如何准确识别出高维数据集间的异同性成为大数据分析的目标之一。本文提出了变系数模型下的高维数据整合分析方法。该方法可以同时对多个数据集进行变量选择和系数估计,并且能 够自动识别出变量系数在数据集间的异同性。模拟结果表明本文方法在异同性识别、变量选择、系数估 计和预测等方面明显优于对比方法。在肺癌致病基因识别的应用研究中,本文方法能够识别出具有生物解释的致病基因并发现了两种亚型之间的异同性。     

A fair evaluation of certain stage in a two-stage structure: revisiting the typical two-stage DEA approaches

The efficiency of decision processes with a two-stage structure has been studied by some modified versions of Data Envelopment Analysis (DEA) methodology, such as the relational or centralized model, and the non-cooperative model. After revisiting the rationale of the centralized model provided by the literature, we find that some unfairness exist in its efficiency evaluation of certain stage. This unfairness leads to the usual underestimation of the overall efficiency by the centralized model. Furthermore, because the independent DEA model for one stage ignores its relation and coordination with the other stage and the two-stage system, externalities between these members may arise and lead to the seeming contradiction in efficiencies derived by independent DEA models for the stages and the black box system. Therefore, we argue that the efficiency of certain stage in the context of a two-stage structure should be reevaluated instead of simply using the independent DEA model. A sequence of leader-follower procedures, with data adjustment on intermediates, is proposed to eliminate the externalities and to ensure a fair evaluation. We find that, after this data adjustment, the reevaluation of the second stage for a given Decision Making Unit (DMU) yields the same result as obtained by the standard DEA model regarding the two-stage as a black box. Moreover, some explicit relations are established between the black box model, the centralized model, and the non-cooperative model. Two typical examples taken from the literature illustrate our main results. Our findings also imply that more emphasis should be placed on the game theoretic DEA approach to model the efficiency evaluation of two-stage processes.