基于物元模型的高速公路交通安全评价体系

为了真实反映高速公路安全的实际情况，基于科学性、可行性、实际性、可比性和定性与定量相结合原则，筛选高速公路交通安全的相关因素，建立高速公路交通安全评价体系。运用物元理论，建立了高速公路交通安全评价的物元模型，该模型以定量数值表示道路交通安全状况属于某等级的程度大小；其中可拓集合关联函数的应用使得道路交通安全的多指标评定更为精细化。江西某高速公路的该模型应用实例表明：该物元模型可较好地给出交通安全等级评定结果，并且能够较完整地反映高速公路交通安全的综合水平。     

联立方程计量经济学模型的结构识别

本文提出了联立方程计量经济学模型结构识别的概念与方法。应用该方法识别模型与统计数据无关,并且与模型参数的具体数值也无关,对计量经济学建模与分析有实际意义.     

我国电力企业信息化建设与核心能力形成相关性实证研究

信息化建设和组织学习都是现代企业核心能力形成的重要因素。我国电力企业的信息化建设与组织学习的开展已进行了很长时期,在取得一定业绩的同时也存在若干障碍因素。对这两种先进管理手段的应用绩效进行实证检验,可以对我国电力企业的管理创新与技术创新提供借鉴。     

企业信息化工程中的匹配理论及映射模型研究

通过对中国企业信息化工程现状的分析,指出先进的信息系统与企业管理基础之间存在较大的差距是项目成功率不高的主要原因,提出企业管理系统与信息系统匹配理论,详细阐述了匹配理论的内容和原则,围绕匹配度测算问题,建立了映射模型,定义了映射函数和映射法则。     

法先王与中国传统治道范式的构建

法先王是中国传统社会权威治道范式。它向往三代的王道理想,崇尚血缘伦理的王权秩序,尊经崇古而又面向现实。法先王以对逝去的历史的理想化诉求为核心构建治道范式,以确立哲学境界层面上的社会理想来表征自己的范式效应,以超越性的意义整合显现其独特价值,成为中国传统政治的灵魂。     

个体行为因素的模糊层次分析

通过构建定性和定量相结合的个体行为因素模糊层次分析模型，探讨个体行为因素对个人绩效的影响主、次问题。先基于人的认知模式建立个体行为层次诊断模型，采用回溯性分析方法来确定个体行为的原因因素或原因组合，再结合模糊层次分析法探讨影响个体行为的主要关联因素并对其进行排序，量化各具体致因因素的权重及其重要性，该方法为改进个体在系统中的绩效提供理论依据，从而为以人为本的工作设计、管理提供有效的决策支持。     

符号网络分析算法及计算机实现

提出了一种分析电路符号网络函数的新编码算法ＮＳＣＡ。它能有效降低基码，防止过早发生整数溢出，从而扩大分析电路的规模。提出了灵敏度分析算法和化简算法，并可对模块电路进行处理，增强了编码算法的适用范围。根据该算法开发了软件系统ＳＮＳＰＣ２．０。实例运行结果证明了算法理论的正确性和可行性     

Bias Reduction through First-order Mean Correction, Bootstrapping and Recursive Mean Adjustment

Standard methods of estimation for autoregressive models are known to be biased in finite samples, which has implications for estimation, hypothesis testing, confidence interval construction and forecasting. Three methods of bias reduction are considered here: first-order bias correction, FOBC, where the total bias is approximated by the O(T^-1) bias; bootstrapping; and recursive mean adjustment, RMA. In addition, we show how first-order bias correction is related to linear bias correction. The practically important case where the AR model includes an unknown linear trend is considered in detail. The fidelity of nominal to actual coverage of confidence intervals is also assessed. A simulation study covers the AR(1) model and a number of extensions based on the empirical AR(p) models fitted by Nelson & Plosser (1982). Overall, which method dominates depends on the criterion adopted: bootstrapping tends to be the best at reducing bias, recursive mean adjustment is best at reducing mean squared error, whilst FOBC does particularly well in maintaining the fidelity of confidence intervals.     

Theoretic and empirical data-inclusive process characterization

Summary.  In process characterization the quality of information that is obtained depends directly on the quality of process model. The current quality revolution is now providing a strong stimulus for rethinking and re-evaluating many statistical ideas. Among these are the role of theoretic knowledge and data in statistical inference and some issues in theoretic–empirical modelling. With this concern the paper takes a broad, pragmatic view of statistical inference to include all aspects of model formulation. The estimation of model parameters traditionally assumes that a model has a prespecified known form and takes no account of possible uncertainty regarding model structure. But in practice model structural uncertainty is a fact of life and is likely to be more serious than other sources of uncertainty which have received far more attention. This is true whether the model is specified on subject-matter grounds or when a model is formulated, fitted and checked on the same data set in an iterative interactive way. For that reason novel modelling techniques have been fashioned for reducing model uncertainty. Using available knowledge for theoretic model elaboration the techniques that have been created approximate the exact unknown process model concurrently by accessible theoretic and polynomial empirical functions. The paper examines the effects of uncertainty for hybrid theoretic–empirical models and, for reducing uncertainty, additive and multiplicative methods of model formulation are fashioned. Such modelling techniques have been successfully applied to perfect a steady flow model for an air gauge sensor. Validation of the models elaborated has revealed that the multiplicative modelling approach allows us to attain a satisfactory model with small discrepancy from empirical evidence.