经济新常态下金融包容与产业结构转型——基于中国省级面板数据的分析

在经济新常态背景下,产业结构面临重大调整,金融包容的融入,使中小企业或个人能够获得价格合理、方便快捷的金融资源,扶持创业创新发展,形成新的增长动力。本文基于1995～2012年中国31个省市的相关数据,运用面板数据模型和分位数回归方法,深入探讨金融包容对产业结构转型的影响。结果表明,金融包容与产业结构转型具有显著的正向关系,并且在不同分位点上金融包容水平对产业结构转型表现出较强的解释力,随着产业结构向高级化发展,金融包容在产业结构转型升级中的作用逐步凸显。经济增长、教育发展、财政支出规模和城市化水平均对产业结构转型具有促进作用,而过多的物质资本投入不利于产业结构转型。     

人体下肢运动能力测评研究

人体在短距离奔跑及跳跃时,足底压力的大小并不仅仅和体重相关,还反映了下肢运动能力的强弱。为了能够客观评价下肢的运动能力,设计了一套测试系统。采用压力传感器对足底压力进行测量,数据通过单片射频收发器件NRF24L01无线模块传输到微机中,记录下不同测试条件下的压力峰值。实验采集了12位测试者在慢走、快跑时的足底压力,分析了足底压力峰值与体重之间的关系。结果表明:压力峰值与体重的比值较小的人,下肢运动能力较弱一些;压力峰值与体重的比值较大的人,下肢运动能力较强一些。根据测评结果,可对人们的身体素质提升提出相关建议,对处于成长期的青少年尤为重要。     

Features selection,data mining and finacial risk classification: a comparative study

The aim of this paper is to compare several predictive models that combine features selection techniques with data mining classifiers in the context of credit risk assessment in terms of accuracy, sensitivity and specificity statistics. The t‐statistic, Battacharrayia statistic, the area between the receiver operating characteristic, Wilcoxon statistic, relative entropy, and genetic algorithms were used for the features selection task. The selected features are used to train the support vector machine (SVM) classifier, backpropagation neural network, radial basis function neural network, linear discriminant analysis and naive Bayes classifier. Results from three datasets using a 10‐fold cross‐validation technique showed that the SVM provides the best accuracy under all features selections techniques adopted in the study for all three datasets. Therefore, the SVM is an attractive classifier to be used in real applications for bankruptcy prediction in corporate finance and financial risk management in financial institutions. In addition, we found that our best results are superior to earlier studies on the same datasets.     

Housing and Health

Deprived housing is recognized as a source of poor health, but there is still little evidence of a causal relationship between housing and health. While existing literature identifies neighborhood effects and the individual dwelling as factors which affect health, it does not offer a joint examination of these factors. Moreover, endogeneity is a concern in analyses of both problems. Thus far, studies addressing endogeneity have done so through experimental design or instrumental variables. The first approach suffers from problems of external validity and the latter from the lack of reliable instruments. We therefore adopt an alternative strategy which considers both sources of endogeneity in order to identify the effects of housing on health by estimating fixed‐effect models. We reveal how housing problems affect health depending on living conditions and socioeconomic status. Our results therefore indicate that living in poor housing is an important short‐term socioeconomic determinant that directly affects health.     

DEA在仓库运作管理中的应用

数据包络分析能对决策单元的有效性进行评价。本文应用数据包络分析的C2R模型,对仓库的运作效率进行研究。选取某部的8个备件仓库为样本,基于C2R模型计算结果,得到一些有价值的仓库运作管理方面的信息。     

Combining Sources of Preference Data for Modeling Complex Decision Processes

We review current state-of-the-art practices for combining preference data from multiple sources and discuss future research possibilities. A central theme is that any one data source (e.g., a scanner panel source) is often insufficient to support tests of complex theories of choice and decision making. Hence, analysts may need to embrace a wider variety of data types and measurement tools than traditionally have been considered in applied decision making and choice research. We discuss the viability of preference-stationarity assumptions usually made when pooling data, as well as random-utility theory-based approaches for combining data sources. We also discuss types of models and data sources likely to be required to make inferences about and estimate models that describe choice dynamics. The latter discussion is speculative insofar as the body of literature on this topic is small.     

工业数据国际标准发展概况

介绍了工业数据国际标准的概况（包括产品技术和制造管理方面）,并对其技术内容及发展趋势进行了简要分析。     

数据挖掘方法在传统预测模型中的应用

数学建模领域中,对于解决不同类型的预测问题有一些经典的预测模型。最近日益流行的数据挖掘技术也有一些针对解决预测问题的独特的数学模型和建模方法。在此对使用传统解析式模型的预测方法与应用数据挖掘技术的数据库模型的预测方法进行了对比,重点分析了2种方法在影响因素的确定、数据规律的寻找方法以及模型侧重点选择上的不同之处,并简单讨论了如何结合2种模型的优点,将数据挖掘技术应用于传统解析式模型的建模过程之中,对已有的解析式预测模型进行改进,进而得出更加可靠准确的预测结果。