A robust version of the hurdlemodel

The excess of zeros is not a rare feature in count data. Statisticians advocate the Poisson-type hurdle model (among other techniques) as an interesting approach to handle this data peculiarity. However, the frequency of gross errors and the complexity intrinsic to some considered phenomena may render this classical model unreliable and too limiting. In this paper, we develop a robust version of the Poisson hurdle model by extending the robust procedure for GLM of Cantoni and Ronchetti (2001) to the truncated Poisson regression model. The performance of the new robust approach is then investigated via a simulation study, a real data application and a sensitivity analysis. The results show the reliability of the new technique in the neighborhood of the truncated Poisson model. This robust modelling approach is therefore a valuable complement to the classical one, providing a tool for reliable statistical conclusions and to take more effective decisions.     

Approximate bounded influence estimation for longitudinal data with outliers and measurement errors

Mixed effects models or random effects models are popular for the analysis of longitudinal data. In practice, longitudinal data are often complex since there may be outliers in both the response and the covariates and there may be measurement errors. The likelihood method is a common approach for these problems but it can be computationally very intensive and sometimes may even be computationally infeasible. In this article, we consider approximate robust methods for nonlinear mixed effects models to simultaneously address outliers and measurement errors. The approximate methods are computationally very efficient. We show the consistency and asymptotic normality of the approximate estimates. The methods can also be extended to missing data problems. An example is used to illustrate the methods and a simulation is conducted to evaluate the methods.     

货币政策与财政政策的分区域产业效应比较

 在具有总量效应的同时,货币政策和财政政策在产业效应方面各自表现如何？本文运用1990～2008年东、中、西部地区的动态面板数据模型进行实证分析,结果显示：东部与中部地区的货币政策和财政政策都存在产业效应,而西部地区货币政策和财政政策的效应均不理想,同时,货币政策和财政政策对三次产业的效应都存在明显的区域性。并且,结合相关理论,文章从产业和区域两个层面对实证结果予以分析,揭示出产业间在增量资本产出率和政府扶持力度以及区域间在银行集中度和基础设施投资方面存在的差异,是实证结果的主要致因。     

Still Partners After All These Years: Ulrich's and ISSN

Since 2000, an Ulrich's Periodicals Directory (Ulrich's) employee has been located in the Library of Congress's U.S. ISSN Center at the Library of Congress to assign ISSN and create records for both Ulrich's and the international ISSN Register/CONSER database. How this arrangement came about, how it has succeeded over the past ten years, and the broad potential for this partnership becoming a model in both organizations for future automated data sharing and data linking is the subject of this article.     

Nonparametric monitoring of equal predictive ability

This paper introduces a novel sequential approach for online surveillance of the equal predictive ability (EPA) hypothesis presumed to hold for many competing forecasting models. A nonparametric control chart is suggested for providing a decision at every new time point as to whether the EPA hypothesis remains valid. The detection ability of our procedure is evaluated in a Monte Carlo simulation study for various types of deviations from the EPA hypothesis. Our approach enables the quick detection of various shift types, is parsimonious, and robust to misspecifications. Based on these results, we formulate practical recommendations for procedure design.     

数据资产证券化探索的法律困境与解决路径

数据已成为推动经济发展的核心驱动力,盘活数据资产价值、促进数据资产运用是当前推进数据要素市场化配置、全面建设数字中国的主要目标。由于数据价值实现进程中受到资金与技术投入的制约,数据潜能释放面临瓶颈。在此背景下,数据资产证券化作为数据资产与证券融资结合的创新业态,能够为企业激活数据资产价值拓展融资渠道、降低融资成本,所以,一经提出便备受关注并持续传递新动向。目前,这一证券新模式正处于初步探索阶段,有必要基于数据资源化到数据资产化再至数据资本化的三重价值演进形态,释明其价值实现的内在理路,即是历经初始生成数据资源到加工获取数据资产再到实现数据资产证券化的动态实践,体现了数据要素价值增值的“变现”之路。在此动态进程中,数据要素的独特属性使得数据资产证券化区别于传统资产证券化,呈现出与新兴科技尤其是区块链技术的深度联系,其技术原生特性有助于促进证券全流程的数字化重塑。通过检视当前“数据+资本”双要素耦合的证券新模式发展现状,发现数据资产证券化探索存在产权规范不明、交易机制缺失、监督管理失配等诸多问题,成为激活数据资产价值的掣肘因素。鉴于此,立足兼顾金融创新与风险防控的二元目标,应从法律与技术协...     

大数据下数字出版编辑的理念和职能创新探析

大数据已经渗透到每个行业和业务职能领域,成为重要的生产要素;大数据正在对数字出版与编辑过程产生深刻的影响。数字出版编辑应与时俱进,转变观念,树立创新理念,准确职能定位,充分利用大数据对信息进行深度加工,以适应市场的需求,履行数字出版编辑的职能。     

基于自适应迭代更新的函数型数据聚类方法研究

函数型数据的稀疏性和无穷维特性使得传统聚类分析失效。针对此问题,本文在界定函数型数据概念与内涵的基础上提出了一种自适应迭代更新聚类分析。首先,基于数据参数信息实现无穷维函数空间向有限维多元空间的过渡;在此基础上,依据变量信息含量的差异构建了自适应赋权聚类统计量,并依此为函数型数据的相似性测度进行初始类别划分;进一步地,在给定阈值限制下,对所有函数的初始类别归属进行自适应迭代更新,将收敛的优化结果作为最终的类别划分。随机模拟和实证检验表明,与现有的同类函数型聚类分析相比,文中方法的分类正确率显著提高,体现了新方法的相对优良性和实际问题应用中的有效性。     

基于理想解法的动态评价方法研究

为了比较多个系统在不同时刻的总体发展水平,需要进行动态评价。将二维数据加入时序数据扩展为三维数据,提出基于理想解法的动态评价方法,该方法既考虑评价指标值差异程度又考虑指标值增长程度,既可以得到各评价对象各时刻的综合评价值及排序,也可以得到各评价对象在某个时间段内总体的评价值和排序结果。最后用一个实例说明本研究实际应用上的有效性。基于理想解的动态评价方法给比较多个系统在不同时刻的总体发展水平(动态评价)提供了思路,是综合评价方法研究的有益补充,可以利用该方法进行应用研究。     

Extending the Mann–Whitney–Wilcoxon rank sum test to longitudinal regression analysis

Outliers are commonly observed in psychosocial research, generally resulting in biased estimates when comparing group differences using popular mean-based models such as the analysis of variance model. Rank-based methods such as the popular Mann–Whitney–Wilcoxon (MWW) rank sum test are more effective to address such outliers. However, available methods for inference are limited to cross-sectional data and cannot be applied to longitudinal studies under missing data. In this paper, we propose a generalized MWW test for comparing multiple groups with covariates within a longitudinal data setting, by utilizing the functional response models. Inference is based on a class of U-statistics-based weighted generalized estimating equations, providing consistent and asymptotically normal estimates not only under complete but missing data as well. The proposed approach is illustrated with both real and simulated study data.