基于可变精度粗糙集的新兴技术企业信用风险识别

针对新兴技术企业信用风险评估的必要性和现有评估方法仅局限于财务指标且指标之间高度相关的缺点,借鉴了可变精度粗糙集（VPRS）模型具有噪声数据的强适应能力和强抗干扰能力的优点,提出了一类基于VPRS的新兴技术企业信用风险识别方法,并用已上市的部分新兴技术企业对其进行实证检验,检验结果表明了该方法具有较好的识别能力。该方法首先运用VPRS理论的最新研究成果,并借助于粗糙集分析软件ROSETYA,对由训练样本组成的数据关系表进行数据补缺、离散化及属性的β约简等处理,从而导出识别规则,形成识别规则库;然后集成二叉树构建一类新兴技术企业信用风险识别方法;最后用测试样本对方法的识别精度进行检验。     

基于代价敏感决策树的客户价值细分(英文)

由于错误分类代价差异和不同价值客户数量的不平衡分布,基于总体准确率的数据挖掘方法不能体现由于客户价值不同对分类效果带来的影响.为了解决错误分类不平衡的数据分类问题,利用代价敏感学习技术扩展现有决策树模型,将这一方法应用在客户价值细分,建立基于客户价值的错分代价矩阵,以分类代价最小化作为决策树分支的标准,建立分类的期望损失函数作为分类效果的评价标准,采用中国某银行的信用卡客户数据进行实验.实验结果表明,与传统决策树方法相比,代价敏感决策树对客户价值细分问题有更好的分类效果,可以更精确地控制代价敏感性和不同种分类错误的分布,降低总体的错误分类代价,使模型能更准确反映分类的代价,有效识别客户价值     

Differing conceptions of the causes of the economic crisis: Effects of culture,economic training,and personal impact

We report findings from a survey regarding the lay perception of the causes of the worldwide economic and financial crisis. Respondents (N = 2245) from a variety of countries were included: China (Hong Kong), Turkey, Russia, Israel, Germany, USA, and France. We have previously documented a range of factors that affects lay understanding of the crisis The present study expanded the database and focuses on the combination of factors that jointly predict whether the respondents view the crisis as a complex impersonal system that malfunctioned, or hold a moral/intentional view about its origins. We show that respondents from Western World countries, who were unaffected by the crisis and have economic training, interpret the crisis differently from all other respondents (i.e., those living in Turkey, Russia, or Hong Kong, and those who were personally affected by the crisis or without economic training). These differences have important implications on how policies are perceived and evaluated by the public, and should inform how they are presented to the public.     

小微企业众筹融资平台风险管理研究

小微企业作为经济发展的重要力量,在发展过程中面临融资难、融资贵等问题,众筹融资作为新兴的网络融资模式不仅给小微企业带来了机遇,也带来了挑战。对小微企业众筹融资整体风险进行识别,提出“5+11”风险识别途径,着重研究众筹融资平台面临的风险,利用系统动力学方法绘制小微企业众筹融资平台风险系统的因果关系图,并根据因果关系图形成回路图和原因树,更直观地展现众筹融资平台风险的反馈作用机制。针对风险提出相应的防范对策,对规范众筹融资平台运作、提高平台融资成功率具有一定的实际意义。     

Statistical Modeling of Fire Occurrence Using Data from the Tōhoku,Japan Earthquake and Tsunami

In this article, we develop statistical models to predict the number and geographic distribution of fires caused by earthquake ground motion and tsunami inundation in Japan. Using new, uniquely large, and consistent data sets from the 2011 Tōhoku earthquake and tsunami, we fitted three types of models—generalized linear models (GLMs), generalized additive models (GAMs), and boosted regression trees (BRTs). This is the first time the latter two have been used in this application. A simple conceptual framework guided identification of candidate covariates. Models were then compared based on their out‐of‐sample predictive power, goodness of fit to the data, ease of implementation, and relative importance of the framework concepts. For the ground motion data set, we recommend a Poisson GAM; for the tsunami data set, a negative binomial (NB) GLM or NB GAM. The best models generate out‐of‐sample predictions of the total number of ignitions in the region within one or two. Prefecture‐level prediction errors average approximately three. All models demonstrate predictive power far superior to four from the literature that were also tested. A nonlinear relationship is apparent between ignitions and ground motion, so for GLMs, which assume a linear response‐covariate relationship, instrumental intensity was the preferred ground motion covariate because it captures part of that nonlinearity. Measures of commercial exposure were preferred over measures of residential exposure for both ground motion and tsunami ignition models. This may vary in other regions, but nevertheless highlights the value of testing alternative measures for each concept. Models with the best predictive power included two or three covariates.     

The one-way analysis of variance with ordered alternatives: A modification of bartholomew's Ē2 Test

Tests of homogeneity of normal means with the alternative restricted by an ordering on the means are considered. The simply ordered case, μ₁ ≤ μ₂ ≤ ··· ≤ μ_k, and the simple tree ordering, μ₁ ≤ μ_j, for; j= 2, 3,…, k, are emphasized. A modification of the likelihood-ratio test is proposed which is asymptotically equivalent to it but is more robust to violations of the hypothesized orderings. The new test has power at the points satisfying the hypothesized ordering which is similar to that of the likelihood-ratio test provided the degrees of freedom are not too small. The modified test is shown to be unbiased and consistent.     

Two-moment approximations to some null distributions in order-restricted inference: Unequal sample sizes

Bartholomew's statistics for testing homogeneity of normal means with ordered alternatives have null distributions which are mixtures of chi-squared or beta distributions according as the variances are known or not. If the sample sizes are not equal, the mixing coefficients can be difficult to compute. For a simple order and a simple tree ordering, approximations to the significance levels of these tests have been developed which are based on patterns in the weight sets. However, for a moderate or large number of means, these approximations can be tedious to implement. Employing the same approach that was used in the development of these approximations, two-moment chisquared and beta approximations are derived for these significance levels. Approximations are also developed for the testing situation in which the order restriction is the null hypothesis. Numerical studies show that in each of the cases the two-moment approximation is quite satisfactory for most practical purposes.     

Simplified Containment Event Tree Methodology Applied to Peach Bottom SBO-ST Sequences1

The methodology presented here identifies an approach to accurately and economically analyze the effects on risk of various containment performance issues. Although this method facilitates the evaluation of potential containment improvements, it does so while utilizing the significant amount of information accumulated by the U.S. NRC Reactor Risk Reference Program. The use of hindsight and the acceptance of point estimate quantifications of risks allows the proposed methodology to be scrutable and understandable to the community as well as relatively simple and inexpensive to apply. A study of containment venting strategies was used to demonstrate the capabilities of the simplified containment event tree methodology. However, the methodology is flexible enough for a wide range of risk evaluations.     

Robust automatic methods for outlier and error detection

Summary.  Editing in surveys of economic populations is often complicated by the fact that outliers due to errors in the data are mixed in with correct, but extreme, data values. We describe and evaluate two automatic techniques for the identification of errors in such long-tailed data distributions. The first is a forward search procedure based on finding a sequence of error-free subsets of the error-contaminated data and then using regression modelling within these subsets to identify errors. The second uses a robust regression tree modelling procedure to identify errors. Both approaches can be implemented on a univariate basis or on a multivariate basis. An application to a business survey data set that contains a mix of extreme errors and true outliers is described.     

Performance of localized regression tree splitting criteria on data with discontinuities

Properties of the localized regression tree splitting criterion, described in Bremner & Taplin (2002) and referred to as the BT method, are explored in this paper and compared to those of Clark & Pregibon's (1992) criterion (the CP method). These properties indicate why the BT method can result in superior trees. This paper shows that the BT method exhibits a weak bias towards edge splits, and the CP method exhibits a strong bias towards central splits in the presence of main effects. A third criterion, called the SM method, that exhibits no bias towards a particular split position is introduced. The SM method is a modification of the BT method that uses more symmetric local means. The BT and SM methods are more likely to split at a discontinuity than the CP method because of their relatively low bias towards particular split positions. The paper shows that the BT and SM methods can be used to discover discontinuities in the data, and that they offer a way of producing a variety of different trees for examination or for tree averaging methods.