A comparison study of computational methods of Kolmogorov–Smirnov statistic in credit scoring

Kolmogorov–Smirnov statistic (KS) is a standard measure in credit scoring. Currently, there are three computational methods of KS: method with equal-width binning, method with equal-size binning and method without binning. This paper compares the three methods in three aspects: Values, Rank Ordering of Scores and Geometrical Way. The computational results on the German Credit Data show that only the method without binning can produce a unique value of KS. It is further proved analytically that the method without binning yields the maximum value of KS among the three methods. The computational results also show that only the method with equal-size binning can be used to evaluate rank ordering of scores. Moreover, it is proved that all the three methods can be used to calculate KS in a geometric way.     

高兹资本主义核电技术批判理论及其当代价值

高兹是法国当代左翼生态政治思想家,他以法国发展核电技术为例,从技术、经济、生态和安全方面对资本主义使用的核电技术进行了深刻的分析和批判。他指出核电技术是"资本的圈套",符合资本主义积累逻辑,是资本主义专制的政治选择,社会生态重建的基本前提是工具转换。高兹的核电批判理论为我们审视目前的能源发展战略提供了一种新的理论视野,他对节约能源以及采用新技术发展新型能源的思考,对建设生态文明具有重要的价值。     

中国服务业显性比较优势测算——基于前向联系增加值出口

基于"前向联系增加值出口"方法,测算了1995-2011年中国服务业细分产业层面及按要素密集度特征分类层面的显性比较优势指数(RCA_F),并同基于"总出口"1的比较优势指数(RCA)进行了比较。研究发现,中国在全球价值链分工中,比较优势集中在资本密集型服务业领域,且实现了比较劣势向比较优势的转变;劳动、知识密集型服务业及健康教育公共服务均尚未取得比较优势,但是有迹象表明三个领域的比较劣势有改善的趋势。与基于前向联系增加值出口核算结果相比,基于总出口核算结果"高估"了劳动密集型产业的比较优势,其中对建筑业和零售贸易两个部门的估计偏差最大;同时,在资本、知识密集型两个领域存在不同程度的"低估"现象。     

A simple algorithm for calculating values for folded normal distribution

Folded normal distribution originates from the modulus of normal distribution. In the present article, we have formulated the cumulative distribution function (cdf) of a folded normal distribution in terms of standard normal cdf and the parameters of the mother normal distribution. Although cdf values of folded normal distribution were earlier tabulated in the literature, we have shown that those values are valid for very particular situations. We have also provided a simple approach to obtain values of the parameters of the mother normal distribution from those of the folded normal distribution. These results find ample application in practice, for example, in obtaining the so-called upper and lower α-points of folded normal distribution, which, in turn, is useful in testing of the hypothesis relating to folded normal distribution and in designing process capability control chart of some process capability indices. A thorough study has been made to compare the performance of the newly developed theory to the existing ones. Some simulated as well as real-life examples have been discussed to supplement the theory developed in this article. Codes (generated by R software) for the theory developed in this article are also presented for the ease of application.     

基于支持向量机的管理信息系统故障识别模型研究

管理信息系统故障影响着企业信息工程的开展。及时识别故障,能够为企业争取更多时间处理故障。因此,管理信息系统故障识别有着重要的研究意义和实践意义。基于支持向量机,构建管理信息系统故障识别模型,通过把样本应用于该模型,结果表明该模型具有较好的准确性,能有效识别管理信息系统故障。     

Machine Learning for Drug Overdose Surveillance

ABSTRACT

We describe two recently proposed machine learning approaches for discovering emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset Scan (Herlands, McFowland, Wilson, & Neill, 2017 Neill, D. B. (2017). Multidimensional tensor scan for drug overdose surveillance. Journal of Public Health Informatics, 9(1), e20. doi:10.5210/ojphi.v9i1.7598[Crossref] , [Google Scholar]) enables early detection of emerging patterns in spatio-temporal data, accounting for both the complex, correlated nature of the data and the fact that detecting subtle patterns requires integration of information across multiple spatial areas and multiple time steps. We apply this approach to 17 years of county-aggregated data for monthly opioid overdose deaths in the New York City metropolitan area, showing clear advantages in the utility of discovered patterns as compared to typical anomaly detection approaches. To detect and characterize emerging overdose patterns that differentially affect a subpopulation of the data, including geographic, demographic, and behavioral patterns (e.g., which combinations of drugs are involved), we apply the Multidimensional Tensor Scan (Neill, 2017 Neill, D. B. (2017). Multidimensional tensor scan for drug overdose surveillance. Journal of Public Health Informatics, 9(1), e20. doi:10.5210/ojphi.v9i1.7598[Crossref] , [Google Scholar]) to 8 years of case-level overdose data from Allegheny County, Pennsylvania. We discover previously unidentified overdose patterns which reveal unusual demographic clusters, show impacts of drug legislation, and demonstrate potential for early detection and targeted intervention. These approaches to early detection of overdose patterns can inform prevention and response efforts, as well as understanding the effects of policy changes.     

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.     

The Interview for Decisional Abilities (IDA): a tool to assess the decisional capacity of abused and neglected older adults

We present the Interview for Decisional Abilities (IDA), a semi-structured tool for use by adult protective services (APS) workers as part of their comprehensive assessments of clients. The IDA was created in response to a Federal mandate to standardize the procedures and competencies of APS agencies with a view to improving client assessments and facilitating cross-jurisdictional research on adult mistreatment. The proximal aim of the IDA is to guide workers in gathering information on the ability of suspected victims of adult mistreatment to make decisions about the risks they face.     

机器学习视角下中国去产能产业的识别

随着中国环境压力的增加,政府提出了供给侧改革,去产能是改革的主要内容,但是由于产业特征的实时演变,需要对政策进行完善。文章运用模糊C均值算法和支持向量机算法分析现阶段需要进行去产能的产业,结果发现在现行去产能政策中大部分行业是需要去产能的,但煤炭开采和洗选业以及铁路、船舶、航空航天和其他运输设备制造业已不适合继续去产能,同时将化学原料和化学制品制造业加入去产能行列中。     

Machine Learning Vasicek Model Calibration with Gaussian Processes

In this article, we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The maximization method used is the conjugate gradients. The only prices needed for calibration are zero coupon bond prices and the parameters are directly obtained in the arbitrage free risk neutral measure.