Use of multiple cluster analysis methods to explore the validity of a community outcomes concept map

Concept mapping is now a commonly-used technique for articulating and evaluating programmatic outcomes. However, research regarding validity of knowledge and outcomes produced with concept mapping is sparse. The current study describes quantitative validity analyses using a concept mapping dataset. We sought to increase the validity of concept mapping evaluation results by running multiple cluster analysis methods and then using several metrics to choose from among solutions. We present four different clustering methods based on analyses using the R statistical software package: partitioning around medoids (PAM), fuzzy analysis (FANNY), agglomerative nesting (AGNES) and divisive analysis (DIANA). We then used the Dunn and Davies-Bouldin indices to assist in choosing a valid cluster solution for a concept mapping outcomes evaluation. We conclude that the validity of the outcomes map is high, based on the analyses described. Finally, we discuss areas for further concept mapping methods research.     

Testing for Serial Independence: Beyond the Portmanteau Approach

Portmanteau tests are typically used to test serial independence even if, by construction, they are generally powerful only in presence of pairwise dependence between lagged variables. In this article, we present a simple statistic defining a new serial independence test, which is able to detect more general forms of dependence. In particular, differently from the Portmanteau tests, the resulting test is powerful also under a dependent process characterized by pairwise independence. A diagram, based on p-values from the proposed test, is introduced to investigate serial dependence. Finally, the effectiveness of the proposal is evaluated in a simulation study and with an application on financial data. Both show that the new test, used in synergy with the existing ones, helps in the identification of the true data-generating process. Supplementary materials for this article are available online.     

Similar coefficient for cluster of probability density functions

In this article, we propose a new criterion to evaluate the similarity of probability density functions (pdfs). We call this the criterion on similar coefficient of cluster (SCC) and use it as a tool to deal with overlap coefficients of pdfs in normal standard on [0;1]. With the support of the self-update algorithm for determining the suitable number of clusters, SCC then becomes a criterion to establish the corresponding cluster for pdfs. Moreover, some results on determination of SCC in case of two and more than two pdfs as well as relations of different SCCs and other measures are presented. The numerical examples in both synthetic data and real data are given not only to illustrate the suitability of proposed theories and algorithms but also to demonstrate the applicability and innovation of the proposed algorithm.     

Phase-II monitoring and diagnosing of multivariate categorical processes using generalized linear test-based control charts

In this paper, two control charts based on the generalized linear test (GLT) and contingency table are proposed for Phase-II monitoring of multivariate categorical processes. The performances of the proposed methods are compared with the exponentially weighted moving average-generalized likelihood ratio test (EWMA-GLRT) control chart proposed in the literature. The results show the better performance of the proposed control charts under moderate and large shifts. Moreover, a new scheme is proposed to identify the parameter responsible for an out-of-control signal. The performance of the proposed diagnosing procedure is evaluated through some simulation experiments.     

Collapsibility of Conditional Graphical Models

Abstract. In this paper, we consider two kinds of collapsibility, that is, the model‐collapsibility and the estimate‐collapsibility, of conditional graphical models for multidimensional contingency tables. We show that these two definitions are equivalent, and propose a sufficient and necessary condition for them in terms of the interaction graph, which allows the collapsibility to be characterized and judged intuitively and conveniently.     

长江三角洲城市群洪涝灾害脆弱性评估及影响因素分析

长江三角洲城市群人口、财产、基础设施高度密集,暴雨洪涝灾害带来的影响越来越大,对该区域城市洪涝灾害脆弱性进行评估是保障长江三角洲区域一体化、可持续发展的基础。以长江三角洲城市群中心区27个城市为研究对象,基于超效率DEA模型,将洪涝灾害脆弱性看作一个负面“生产活动”,构建城市洪涝灾害脆弱性评估模型,在分析脆弱性空间分异特征的基础上确定该地区城市洪涝灾害脆弱性的主要影响因素。结果显示：从整体上看,长江三角洲城市群洪涝灾害脆弱性水平差异较大,安徽及浙江远高于江苏和上海,其中,上海的洪灾脆弱性最低;从空间上看,脆弱性分布有明显的地域集聚特征,脆弱性高的城市分布在浙江东南沿海和安徽南部丘陵地区;降雨量、城市发展水平及基础设施的完善性等因素对城市洪涝灾害脆弱性影响显著。     

实物资金流量表的预测方法研究

资金流量表是国民经济核算体系中的重要组成部分。然而,由于在编制过程中需要采集大量的数据,通常情况下,很多国家的资金流量表都会有较长时间的滞后。在编制实物资金流量表的延长表时,已有方法通常是基于基期与预测期交易收支结构保持不变的假定条件。然而,经济结构发生显著变化时,该类方法就会失效。基于上述问题,研究弱化模型的假设条件,并提出了新的实物资金流量表预测方法( 简称 FPTF方法)。根据表中元素必须满足的约束条件,该方法通过建立数学模型解除约束,其次基于历史数据的动态趋势,采用适当的时间序列分析方法来预测目标年份的实物资金流量表。通过仿真分析,验证了所提方法的有效性和稳定性。此外,基于中国1992年～2014 年的实物资金流量表数据进行实例分析,取得了满意的分析结果。     

我国制造业集聚变迁与全要素生产率增长研究

本文选取我国29个省市区和17个制造行业1990～2009年的面板数据,对相对专业化与相对多样化集聚度进行了计算及变迁分析,利用Malmquist指数测算了全要素生产率的增长,并分析了产业集聚变迁对全要素生产率的影响。结果表明:我国东部地区呈现多样化与专业化集聚均衡发展的特征,而中西部地区则具有相对较高的专业化集聚水平,呈现产业结构单一化与非均衡化;东部地区全要素生产率增长较快,中西部地区相对滞后;相对专业化与多样化集聚对东部、中部与西部地区的影响存在地区差异。基于此,本文提出了加快产业结构调整、实施均衡的区域发展政策和提高经济组织制度绩效的政策建议。     

网络与集群:温州企业家群体形成的机制分析

同历史上地域性企业家集群(商帮)类似,温州呈现了一种独有的企业家集群的现象.本文首先给出了温州企业家集群的整体描述,然后例举了三种典型的温州企业家集群.通过企业家集群和社会网络研究的综述,以及对温州企业家集群形成的历程分析,提出了五个基于社会网络理论的基本假设,认为温州企业家集群出现的主要原因在于“瓯文化”,“瓯文化”是温州企业家精神和企业家“干中学”才能形成的渊源.     

Invariant co-ordinate selection

Summary.  A general method for exploring multivariate data by comparing different estimates of multivariate scatter is presented. The method is based on the eigenvalue–eigenvector decomposition of one scatter matrix relative to another. In particular, it is shown that the eigenvectors can be used to generate an affine invariant co-ordinate system for the multivariate data. Consequently, we view this method as a method for invariant co-ordinate selection . By plotting the data with respect to this new invariant co-ordinate system, various data structures can be revealed. For example, under certain independent components models, it is shown that the invariant co- ordinates correspond to the independent components. Another example pertains to mixtures of elliptical distributions. In this case, it is shown that a subset of the invariant co-ordinates corresponds to Fisher's linear discriminant subspace, even though the class identifications of the data points are unknown. Some illustrative examples are given.