Detection of outliers in high-dimensional data using nu-support vector regression

Support Vector Regression (SVR) is gaining in popularity in the detection of outliers and classification problems in high-dimensional data (HDD) as this technique does not require the data to be of full rank. In real application, most of the data are of high dimensional. Classification of high-dimensional data is needed in applied sciences, in particular, as it is important to discriminate cancerous cells from non-cancerous cells. It is also imperative that outliers are identified before constructing a model on the relationship between the dependent and independent variables to avoid misleading interpretations about the fitting of a model. The standard SVR and the μ-ε-SVR are able to detect outliers; however, they are computationally expensive. The fixed parameters support vector regression (FP-ε-SVR) was put forward to remedy this issue. However, the FP-ε-SVR using ε-SVR is not very successful in identifying outliers. In this article, we propose an alternative method to detect outliers i.e. by employing nu-SVR. The merit of our proposed method is confirmed by three real examples and the Monte Carlo simulation. The results show that our proposed nu-SVR method is very successful in identifying outliers under a variety of situations, and with less computational running time.     

对集体所有乡镇企业从业人员数量统计偏差的研究

乡镇企业统计数据的质量关系到很多国民经济指标的测算 ,因而有着十分重要的意义。将乡镇企业中集体企业从业人员数量的统计数据与第一次全国农业普查结果相比较 ,发现二者有较大的差距。该研究通过定性和定量分析 ,测定了这一统计偏差的幅度 ,并尝试找出集体所有乡镇企业从业人员数量的统计偏差与影响因素之间的关系 ,根据该项研究结果 ,最后提出了一些政策建议     

The Work Profiler: Revision and maintenance of a profiling tool for the recently unemployed in the Netherlands

For the public employment services of many Member countries of the Organisation for Economic Co-operation and Development, the importance of using profiling tools for job seekers is increasing rapidly in importance. With this trend, there is also widening concern about the risks of an over reliance on such tools. Part of the concern lies with a lack of transparency concerning how such tools work. This article aims to address this by offering a detailed investigation of the Work Profiler – the instrument used in the Netherlands by the Institute for Employee Benefits (Uitvoeringsinstituut Werknemersverzekeringen – UWV) to predict re-employment success and provide a diagnosis of key factors hindering job seekers’ return to work. Professionals use these insights to deepen their understanding of the situation of job seekers and decide together with job seekers how to support their return to work. UWV decided to maintain and revise the Work Profiler through a large-scale study involving a sample of 53,238 people. Work Profiler 1.0 was developed in 2007–2010 and has been in use on a regional basis since 2011 and nationwide since 2015. This article explains how the new tool (version 2.0; implemented in 2018) works and, most importantly, demonstrates the choices made to ensure that it functions well and is used effectively by professionals. These latter two aspects are rarely discussed in the literature.     

Divergence versus decision P-values: A distinction worth making in theory and keeping in practice: Or,how divergence P-values measure evidence even when decision P-values do not

There are two distinct definitions of “P-value” for evaluating a proposed hypothesis or model for the process generating an observed dataset. The original definition starts with a measure of the divergence of the dataset from what was expected under the model, such as a sum of squares or a deviance statistic. A P-value is then the ordinal location of the measure in a reference distribution computed from the model and the data, and is treated as a unit-scaled index of compatibility between the data and the model. In the other definition, a P-value is a random variable on the unit interval whose realizations can be compared to a cutoff α to generate a decision rule with known error rates under the model and specific alternatives. It is commonly assumed that realizations of such decision P-values always correspond to divergence P-values. But this need not be so: Decision P-values can violate intuitive single-sample coherence criteria where divergence P-values do not. It is thus argued that divergence and decision P-values should be carefully distinguished in teaching, and that divergence P-values are the relevant choice when the analysis goal is to summarize evidence rather than implement a decision rule.     

Leading beyond regulatory approval: Opportunities for statisticians to optimize evidence generation and impact clinical practice

The role and value of statistical contributions in drug development up to the point of health authority approval are well understood. But health authority approval is only a true ‘win’ if the evidence enables access and adoption into clinical practice. In today's complex and evolving healthcare environment, there is additional strategic evidence generation, communication, and decision support that can benefit from statistical contributions. In this article, we describe the history of medical affairs in the context of drug development, the factors driving post-approval evidence generation needs, and the opportunities for statisticians to optimize evidence generation for stakeholders beyond health authorities in order to ensure that new medicines reach appropriate patients.