Variable precision rough set theory and data discretisation: an application to corporate failure prediction

Since the seminal work of Pawlak (International Journal of Information and Computer Science, 11 (1982) 341–356) rough set theory (RST) has evolved into a rule-based decision-making technique. To date, however, relatively little empirical research has been conducted on the efficacy of the rough set approach in the context of business and finance applications. This paper extends previous research by employing a development of RST, namely the variable precision rough sets (VPRS) model, in an experiment to predict between failed and non-failed UK companies. It also utilizes the FUSINTER discretisation method which neglates the influence of an ‘expert’ opinion. The results of the VPRS analysis are compared to those generated by the classical logit and multivariate discriminant analysis, together with more closely related non-parametric decision tree methods. It is concluded that VPRS is a promising addition to existing methods in that it is a practical tool, which generates explicit probabilistic rules from a given information system, with the rules offering the decision maker informative insights into classification problems.     

大数据环境下商业信息服务模式创新研究

在现代商业活动中,由于供需双方之间的信息不对称,使得大量潜在交易无法实施,降低了市场运行的效率。本文指出了消除信息不对称的关键,并基于大数据技术发展动态,提出了一种创新的商业信息服务模式,深入分析了其中所包括的主体及其主要作用,并给出了相应的运行机制。     

数据挖掘在家电企业绩效评价中的应用研究

选取我国家电行业上市公司2008年度数据,利用主成分分析方法对财务比率指标数据进行分析,提取盈利能力、发展能力、偿债能力及资产营运能力四个主要因子,并结合因子在各财务指标的载荷以及各财务指标之间的复相关系数来确定财务指标的选取和权重的设定,以此构建企业绩效评价指标体系,然后利用所构建的指标体系对家电行业上市公司的企业绩效进行评测排序,最后利用聚类分析方法检验该指标体系的有效性。研究结果表明,使用此种方法建立企业绩效评价体系是可行有效的。     

“数据结构”双语教学中的问题与对策

“数据结构”作为软件工程专业一门重要的专业基础课程,旨在通过研究数据在计算机中的表示、组织和处理来提高算法分析和程序设计能力。双语教学作为对该课程采用的一种教学方式,在当前的教学实践过程中存在着一些影响教学效果的问题。文章通过对这些问题的分析,结合具体的教学实践研究其对策,从教学模式的选择、各种教学方法和手段的运用等方面提出了相应的改进措施,以提高教学质量,并为计算机专业其他课程的双语教学提供一定的参考。     

基于DEA方法的我国中小保险企业核心竞争力研究

随着国内保险市场的逐步开放,我国保险企业特别是中小保险企业面临着越来越大的竞争压力,提高核心竞争力成为中小保险企业持续健康发展的必然选择。DEA（数据包络分析）方法作为管理科学与系统工程领域一项重要而有效的分析工具,利用C^2R模型和投入产出指标体系,对输入输出数据进行综合分析,提炼出综合效率的数量指标,并将决策因素进行定级排序,从而给决策者提供高效率的管理信息,为提升我国中小保险企业核心竞争力提供有效对策。     

《明伦大典》的编纂始末

文章详细介绍了嘉靖《明伦大典》的编纂过程,该书由《大礼奏议》到《明伦大典》最后成书,历时四年,几易其稿,经数人之手,是嘉靖时期的一部重要的史料,对大礼议及嘉靖政治研究有重大意义。笔者通过分析该书的编纂原因、编纂过程以及内容上前后数稿的异同,深入发掘了该书的重要意义与史料价值。     

Improved methods for moment restriction models with data combination and an application to two-sample instrumental variable estimation

Combining-100 information from multiple samples is often needed in biomedical and economic studies, but differences between these samples must be appropriately taken into account in the analysis of the combined data. We study the estimation for moment restriction models with data combined from two samples under an ignorability-type assumption while allowing for different marginal distributions of variables common to both samples. Suppose that an outcome regression (OR) model and a propensity score (PS) model are specified. By leveraging semi-parametric efficiency theory, we derive an augmented inverse probability-weighted (AIPW) estimator that is locally efficient and doubly robust with respect to these models. Furthermore, we develop calibrated regression and likelihood estimators that are not only locally efficient and doubly robust but also intrinsically efficient in achieving smaller variances than the AIPW estimator when the PS model is correctly specified but the OR model may be mispecified. As an important application, we study the two-sample instrumental variable problem and derive the corresponding estimators while allowing for incompatible distributions of variables common to the two samples. Finally, we provide a simulation study and an econometric application on public housing projects to demonstrate the superior performance of our improved estimators. The Canadian Journal of Statistics 48: 259–284; 2020 © 2019 Statistical Society of Canada     

异质性大数据的分布式估计

随着物联网技术的进步,大数据给网络带宽和计算机存储能力带来巨大挑战,传统的集中式数据处理难以实现,客观上促进了分布式统计学习的发展。在无迭代算法研究中,Zhang等(2013)证明了当数据集个数s=O(N) 时,基于局部经验风险最小化的分治(DC)简单平均估计量具有O(N-1)均方误差收敛速度,Huang和Huo(2019)在M估计框架下进一步提出分布式一步估计量,但上述方法均未考虑海量数据可能存在的异质性对分治估计效果的影响。本文在线性模型框架下提出海量异质数据的分治一步加权估计,证明了估计量的渐近性质并考虑了异质性检验问题。将本文提出的方法应用于美国医疗保险实际数据分析,结果表明该方法能更好地拟合数据的线性趋势且显著提高了计算效率。     

大规模数据下基于充分降维的Leverage重要性抽样方法

随着信息技术的飞速发展，大规模数据在短时间内搜集并储存下来，为分析决策提供了巨大的信息量，也给统计建模带来了一定难度。对于样本容量大、变量个数少的数据，Leverage重要性抽样是一个简便可行的方法。本文发现，该方法中度量样本重要性的Leverage分数与因变量无关，而且在维度较大的情形下对样本没有区分程度，使得估计结果较差。为了同时考虑因变量和维度的影响，本文提出了基于充分降维的Leverage重要性抽样方法。该方法以不损失信息为前提，在充分降维的空间内重新计算Leverage分数，使得抽样更具有代表性。模拟数据分析显示，在样本容量较大的复杂数据中，相比于原始的Leverage重要性抽样方法，本文提出的方法可以降低估计的均方误差。三个实际数据也证实了该方法的可行性和有效性。     

Efficiency and Productivity changes in the presence of undesirable outcomes in Emergency Obstetric and Newborn Care

This paper analyzes efficiency and productivity changes in Emergency Obstetric and Newborn care (EmONC) in regional public hospitals in India. A novel approach of Weighted Russell Directional Distance Function Model (WRDDFM) has been employed. This model considers all the radial and non-radial slacks associated with good outputs as well as the undesirable outcomes while estimating inefficiency and helps to identify the source of the inefficiency. The empirical results based on a sample of 46 regional public hospitals from 2006–07 to 2013–14 show on average overall inefficiency of 24% and much of these are due to inefficiencies associated with undesirable outcome of stillbirths and intra-uterine deaths. Further, on an average the sampled hospitals have experienced marginal productivity decline over the reform period. The study indicates that there is a greater scope for efficiency and productivity gains in public hospitals, particularly if the undesirable outcome of still birth is minimized.