制造业项目结构分解过程模型研究

针对项目分解中工作分解结构方法的不足,以制造业项目为例,考虑项目产品本身的构造及特点的基础上,综合项目产品的工艺装配过程、产品数据管理技术以及项目组织结构,构建了项目结构分解的过程模型,并对该模型进行了详细分析,应用该模型能够更加科学、合理地进行项目分解,确定项目范围。     

企业管理信息系统与P3、P3e/c数据交换的研究与实现

本文首先讨论P3、P3e/c项目管理软件与企业管理信息系统集成的必要性和重要性,然后详细地介绍了采用RA引擎(OLE对象)技术来实现P3软件与企业管理信息系统的数据交换,以及与P3e/c交互的原理。分析P3、P3e/c在数据组织上的差异,提出解决方案。最后以实例讨论和分析了这种方法在实际系统中的应用。     

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.     

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.     

通风机性能的微机全自动测试与数据处理系统

介绍一种通风机性能微机全自动测试与数据处理系统,阐述了该系统的组成和功能,传感器和主要设备的选用,系统软件的设计及提高系统测试精度所采用的方法。工业应用证实该系统性能良好。     

Bivariate frailty model for the analysis of multivariate survival time

Because of limitations of the univariate frailty model in analysis of multivariate survival data, a bivariate frailty model is introduced for the analysis of bivariate survival data. This provides tremendous flexibility especially in allowing negative associations between subjects within the same cluster. The approach involves incorporating into the model two possibly correlated frailties for each cluster. The bivariate lognormal distribution is used as the frailty distribution. The model is then generalized to multivariate survival data with two distinguished groups and also to alternating process data. A modified EM algorithm is developed with no requirement of specification of the baseline hazards. The estimators are generalized maximum likelihood estimators with subject-specific interpretation. The model is applied to a mental health study on evaluation of health policy effects for inpatient psychiatric care.