基于多项式样条回归的基尼系数的算法

基尼系数的测算目前有分布函数法、几何计算法与曲线拟合法。本文推导出洛伦兹曲线的三次分段多项式样条函数拟合的简单表达式,再通过样条回归确定系数,从而可以比较精确地求出基尼系数,并在此基础上进行了实证分析,且计算结果比文献[6]的结果要精确。     

An Intuitive Test of Structural Change

This note suggests a simple, intuitive approach to testing for structural changes in time series data. The procedure is illustrated using historical data from a recent issue of Teaching Statistics.     

Learning,the Future,and Complexity. An Essay on the Emergence of Futures Literacy

Futures Literacy is the capacity to design and implement processes that make use of anticipation, generally with the purpose of trying to understand and act in a complex emergent context. This article examines the potential of Futures Literacy to contribute to the realisation of a better balance between learning that is shaped by the supposition that what needs to be learned is knowable in advance, what I will label ‘push’ education, and ‘pull’ learning, that starts from the discovery of not knowing something, initiating the search for hypotheses, experiments, and evidence that eventually lead to understanding. Insufficient Futures Literacy impedes the expansion our anticipatory activities beyond preparation and planning, with the result that at both the individual and institutional levels it is difficult to find the motivation and capability to undertake and organise learning that goes beyond ‘push’ education, or what people ‘need’ to know now in order to get: a ‘good job’, be ‘good citizens’, etc., in the future. As a result humanity may be less able to embrace complexity or pursue a diversification approach to resilience.     

经济增长促进我国档案发展的实证研究

随着信息技术的进步,我国档案业面临着新的发展趋势。运用因子分析法对我国档案发展现状进行实证研究,结果表明除2005和2012年,国家档案业呈稳固上升发展趋势。回归分析和格兰杰因果关系检验结果显示,我国档案业发展不是推动经济增长的原因,而经济增长是促进我国档案业发展的原因,二者之间存在单向因果关系。从当前我国档案管理中存在的问题入手,提出若干发展建议。     

时序与回归预测方法比较研究

基于神经网络的时序和回归预测方法,对同一数据建立BP、RBF神经网络及小波神经网络(WNN)模型,分别采用两种方法预测,比较实验结果,指出各自的优点和存在的问题。仿真实验表明回归预测法高于时序预测精度,但回归预测法要求一定规模的数据且规律性好,它需提供各影响因子数据才能进行并行递推预测;时序预测一般精度较差,但数据采集方便,其可行性和实用性好,适用于数据信息少的环境等特点。     

A Demonstration of Regression False Positive Selection in Data Mining

Business analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection (type I errors) is nearly certain to occur. This article describes an in‐class demonstration that shows the frequency and impact of false positives on data mining regression‐based predictive modeling. In this demonstration, 500 randomly generated independent (X) variables are individually regressed against a single, randomly generated (Y) variable, and the resulting 500 p‐values are sorted and examined. This experiment is repeated and the distribution of the number of variables significant at the 5% level resulting from this simulation is presented and discussed. The demonstration provides a tangible example in which students see the reality and risks of incorrectly inferring statistical significance of independent regression variables. Students have expressed a deeper understanding and appreciation of the risks of type I errors through this demonstration. This demonstration is innovative because the scale of the simulation allows the students to experience the near certainty that the correlations shown in the results are truly random.     

Applying regression models to query-focused multi-document summarization

Most existing research on applying machine learning techniques to document summarization explores either classification models or learning-to-rank models. This paper presents our recent study on how to apply a different kind of learning models, namely regression models, to query-focused multi-document summarization. We choose to use Support Vector Regression (SVR) to estimate the importance of a sentence in a document set to be summarized through a set of pre-defined features. In order to learn the regression models, we propose several methods to construct the “pseudo” training data by assigning each sentence with a “nearly true” importance score calculated with the human summaries that have been provided for the corresponding document set. A series of evaluations on the DUC data sets are conducted to examine the efficiency and the robustness of the proposed approaches. When compared with classification models and ranking models, regression models are consistently preferable.     

基于FitNesse的自动化回归测试工具的设计与实现

回归测试是软件测试生命周期中的一个十分重要且成本昂贵的过程。在FitNesse基础上,设计并实现了一种自动化回归测试工具（Automated Regression Testing Tool,以下简称ARTT）,该工具解析被测试系统SUT（SystemUnder Test）,自动生成运行在FitNesse环境下的测试用例框架及测试代码。实验结果表明,该工具对减少回归测试成本,提高回归测试效率具有十分重要的意义。     

基于因子分析法构建世界优秀女子沙滩排球运动员运动成绩回归模型

目的：探讨世界女子沙滩排球运动员基本情况、运动技术与综合实力（因子得分）之间的关系;构建综合实力（因子得分）与运动成绩间最优模型。方法：以2012年伦敦奥运会女子沙滩排球项目所有参赛的24对（共计48名）运动员为研究对象,采用因子分析法探讨运动员基本情况、运动技术与综合实力（因子得分）之间的关系,采用回归法构建综合实力（因子得分）与运动成绩间最优模型。结果：（1）基本技术因子、主动得分因子、基本条件因子和体型因子分别占世界女子沙滩排球运动员基本情况、运动技术构成比例的49.9%,13.8%,12.7%和11.3%。（2）对三个回归模型和模型偏回归系数进行检验,发现因子得分3和因子得分4不能被引入模型。结论：（1）世界女子沙滩排球运动员基本情况、运动技术构成主要体现在基本技术因子和主动得分因子。（2）综合实力（因子得分）与运动成绩间的最优回归模型应以因子得分1和因子得分2为自变量,奥运名次为因变量来构建的多元线性回归方程。     

基于微观非实验数据的政策效应评估方法评价与比较

社会政策是政府发挥职能的重要手段,对政策实施的效果进行量化评估,有助于政府决策的科学化、合理化。本文基于非实验数据,归纳了目前微观政策领域各种致力于构建"反事实"的政策效应评估方法,包括工具变量法(IV)、断点回归(RD)、双重差分(DID)和匹配方法(Matching),阐述了其各自的优势、劣势及适用条件,并对实际政策评估中各模型的选用进行了简要述评。