Poverty,Government Policy and Public Opinion in Britain and Israel: A Comparative Analysis

What are the effects of public opinion on social welfare policies? To what extent is increased financial strain associated with stronger support for anti‐poverty policies? This article tests welfare state theory by comparing poverty, government policy and public opinion on poverty in the UK and Israel, based on rich and detailed comparable survey data on the conditions of the poor in both countries. The results show that, despite similar levels of income poverty and inequality, the poor have a considerably lower standard of living in Israel than the UK. There is considerably greater public support for the poor in Israel but limited government action to end poverty, whereas in the UK there is more limited public support for the poor but significant government action on poverty and exclusion. In both countries there appears to be only a limited connection between social solidarity with the poor and the political elite's rhetoric and action.     

Nonparametric depth-based multivariate outlier identifiers, and masking robustness properties

In extending univariate outlier detection methods to higher dimension, various issues arise: limited visualization methods, inadequacy of marginal methods, lack of a natural order, limited parametric modeling, and, when using Mahalanobis distance, restriction to ellipsoidal contours. To address and overcome such limitations, we introduce nonparametric multivariate outlier identifiers based on multivariate depth functions, which can generate contours following the shape of the data set. Also, we study masking robustness, that is, robustness against misidentification of outliers as nonoutliers. In particular, we define a masking breakdown point (MBP), adapting to our setting certain ideas of Davies and Gather [1993. The identification of multiple outliers (with discussion). Journal of the American Statistical Association 88, 782–801] and Becker and Gather [1999. The masking breakdown point of multivariate outlier identification rules. Journal of the American Statistical Association 94, 947–955] based on the Mahalanobis distance outlyingness. We then compare four affine invariant outlier detection procedures, based on Mahalanobis distance, halfspace or Tukey depth, projection depth, and “Mahalanobis spatial” depth. For the goal of threshold type outlier detection, it is found that the Mahalanobis distance and projection procedures are distinctly superior in performance, each with very high MBP, while the halfspace approach is quite inferior. When a moderate MBP suffices, the Mahalanobis spatial procedure is competitive in view of its contours not constrained to be elliptical and its computational burden relatively mild. A small sampling experiment yields findings completely in accordance with the theoretical comparisons. While these four depth procedures are relatively comparable for the purpose of robust affine equivariant location estimation, the halfspace depth is not competitive with the others for the quite different goal of robust setting of an outlyingness threshold.     

RFID as an Enabler of Improved Manufacturing Performance*

The use of radio frequency identification (RFID) versus bar coding has been debated with little quantitative research about how to best use RFID's capabilities and when RFID is more advantageous. This article responds to that need by qualitatively and quantitatively analyzing how RFID facilitates increased traceability and control in manufacturing, which in turn enables the use of more lot splitting and smaller lot sizes. We develop insights about operating policies (RFID vs. bar‐code tracking mechanisms, extent of lot splitting, and dispatching rules) and an operating condition (setup to processing time ratio) that affect the mean flow time and proportion of jobs tardy in a job shop. A simulation model is used to control factors in the experimental design and the output is evaluated using analysis of variance. The results show the following: (i) performance worsens when bar coding is used with extensive lot splitting, (ii) process changes such as extensive lot splitting may be required to justify RFID use instead of bar coding, (iii) the earliest operation due date dispatching rule offers an attractive alternative to other rules studied in previous lot splitting research, and (iv) the performance improvements with RFID and increased lot splitting are larger when the setup to processing time ratio is smaller. In a broader context, we fill a research void by quantitatively showing how RFID can be used as an advanced manufacturing technology that enables more factory automation and better performance along several dimensions. The article concludes by summarizing the results and identifying ideas for future research.     

Models for measuring and benchmarking olympics achievements

As demonstrated in several recent studies, data envelopment analysis (DEA) is a useful tool for evaluating and comparing the performance of nations competing in the Olympic Games. Assurance regions (ARs) have been used to further refine the DEA results. These AR DEA models assume that ARs apply uniformly across all nations. Such models can have shortcomings in the sense that different nations may impose different ARs, as nations may value gold, silver, and bronze medals differently. This paper extends previous DEA studies by incorporating multiple sets of nation-specific ARs into the DEA. By doing so, we establish fair models for measuring and benchmarking the performance of nations at six summer Olympic Games.     

A generalisation of the Farrell cost efficiency measure applicable to non-fully competitive settings

This paper enhances cost efficiency measurement methods to account for situations where the input prices can depend on negotiation or tend to qualitatively differentiate the resources available at each decision making unit. In these circumstances, there are some shortcomings in the cost efficiency measure described in the data envelopment analysis (DEA) literature. This paper proposes new models and measures for cost efficiency evaluation that overcome the limitations of the existing DEA models. The applicability of the measures developed is illustrated in the context of the analysis of bank branch performance.     

Novel quadratic programming approach for time series clustering with biomedical application

Fundamental problems in data mining mainly involve discrete decisions based on numerical analyses of data (e.g., class assignment, feature selection, data categorization, identifying outlier samples). These decision-making problems in data mining are combinatorial in nature and can naturally be formulated as discrete optimization problems. One of the most widely studied problems in data mining is clustering. In this paper, we propose a new optimization model for hierarchical clustering based on quadratic programming and later show that this model is compact and scalable. Application of this clustering technique in epilepsy, the second most common brain disorder, is a case point in this study. In our empirical study, we will apply the proposed clustering technique to treatment problems in epilepsy through the brain dynamics analysis of electroencephalogram (EEG) recordings. This study is a proof of concept of our hypothesis that epileptic brains tend to be more synchronized (clustered) during the period before a seizure than a normal period. The results of this study suggest that data mining research might be able to revolutionize current diagnosis and treatment of epilepsy as well as give a greater understanding of brain functions (and other complex systems) from a system perspective. This work was partially supported by the NSF grant CCF 0546574 and Rutgers Research Council grant-202018.     

非平衡数据集的改进SMOTE再抽样算法

非平衡数据集的不均衡学习特点通常表现为负类的分类效果不理想。改进SMOTE再抽样算法,将过抽样和欠抽样方式有机结合,有针对性地选择近邻并采用不同策略合成样本。实验表明,分类器在经此算法处理后的非平衡数据集的正负两类上,均可获得较理想的分类效果。     

三阶段DEA模型管理无效率估计注记

由于能够同时调整外部环境与随机误差等因素对效率计算的影响,由Fried et al.(2002)提出的三阶段DEA模型得到了广泛应用。但在对其中的管理无效率进行估计时,国内一些学者误用了Jondrow et al. (1982)的公式。本文给出了适用公式,并进一步给出了使用李双杰等(2007)提出的统一分布假设的三阶段DEA模型管理无效率的估计公式。     

基于Bayes后验概率的自变量与异常点的同时识别

本文基于自变量与异常点识别隐变量的联合Bayes后验概率,给出了自变量与异常点同时识别的一般方法,且利用Gibbs抽样降低了Bayes后验概率的计算复杂度。其次,针对多值序次数据模型自变量与异常点的同时识别展开详细讨论,给出了同时识别的具体过程。最后通过模拟算例展示了本文方法的有效性。     

大数据推动政府治理创新的目标与路径

大数据的广泛应用对社会产生了深远影响,也对政府治理变革起着重要推动作用,大数据将推动政府治理理念、治理体系、治理方式的创新。政府治理的目标就是应用大数据实现法治政府、创新政府、廉洁政府和服务型政府。在运用大数据推动政府治理创新的过程中,应通过数据共享优化政府治理结构,通过政务形态信息化调整政府治理关系,通过政务平台技术化重塑政府流程,借用大数据来提升政府创新能力,通过数据应用法制化提升政府法治水平。所以,政府必须要主动适应信息公开和数据共享的大趋势,以此推动政府治理的变革与创新,进一步提高政府治理能力。