构建电子政务系统的研究

文章介绍了电子政务的内涵及其基本内容 ,分析了实施电子政务的意义 ,指出了实施电子政务的基本原则 ,提出了构建电子政务系统的网络模型和层次模型。     

Empirical likelihood confidence intervals for the mean of a population containing many zero values

If a population contains many zero values and the sample size is not very large, the traditional normal approximation‐based confidence intervals for the population mean may have poor coverage probabilities. This problem is substantially reduced by constructing parametric likelihood ratio intervals when an appropriate mixture model can be found. In the context of survey sampling, however, there is a general preference for making minimal assumptions about the population under study. The authors have therefore investigated the coverage properties of nonparametric empirical likelihood confidence intervals for the population mean. They show that under a variety of hypothetical populations, these intervals often outperformed parametric likelihood intervals by having more balanced coverage rates and larger lower bounds. The authors illustrate their methodology using data from the Canadian Labour Force Survey for the year 2000.     

期权定价理论在技术商品定价中的应用探讨

技术商品不同于一般商品 ,具有报酬无限、风险有限的特性 ,尤其与支付红利的美式看涨期权特点极为相似。应用传统的资产评估方法为技术商品定价 ,其结果很难真实反映技术商品的价值。期权定价理论为技术商品的科学定价提供了一种新思路 ,特别是支付红利的美式看涨期权定价模型对解决技术商品价值评估有参考价值     

Bayesian analysis of mixture modelling using the multivariate t distribution

A finite mixture model using the multivariate t distribution has been shown as a robust extension of normal mixtures. In this paper, we present a Bayesian approach for inference about parameters of t-mixture models. The specifications of prior distributions are weakly informative to avoid causing nonintegrable posterior distributions. We present two efficient EM-type algorithms for computing the joint posterior mode with the observed data and an incomplete future vector as the sample. Markov chain Monte Carlo sampling schemes are also developed to obtain the target posterior distribution of parameters. The advantages of Bayesian approach over the maximum likelihood method are demonstrated via a set of real data.     

群体决策支持系统的模型库研究

通过对传统模型及模型库的分析,指出其在网络环境下具有难以实现共享的不足.从模型在网络上共享及其软件实现的角度,在分布式网络数据库环境下,利用分布式网络数据库技术,设计了模型库的结构和机理;提出了模型库的构造方法;对模型库的内部控制方式进行了设计;提出了模型库对群体决策的支持方式;客户端的决策成员对模型库的共享与访问方法.     

高师数学教育专业课程体系的改革探索

在分析师专数学专业课程改革因素的基础上，探讨了课程体系改革的原则，确定了师专数学专业新的培养目标、专业规格，提出了课程体系改革的初步方案。     

我国深化素质教育改革的主要对策

目前我国素质教育改革的重点应是尽快启动素质教育评价体系建设国家研究项目,积极推进高校招生制度改革,促进学生身心协调健康成长,加快推进中小学实施小班化教学和加强农村师资建设。力争在较短的时间内实现高等教育质量的根本性恢复,稳步推进教育管理体制改革为素质教育改革目标的实现提供制度环境支持。     

A Bayesian Adjustment for Covariate Misclassification with Correlated Binary Outcome Data

Estimated associations between an outcome variable and misclassified covariates tend to be biased when the methods of estimation that ignore the classification error are applied. Available methods to account for misclassification often require the use of a validation sample (i.e. a gold standard). In practice, however, such a gold standard may be unavailable or impractical. We propose a Bayesian approach to adjust for misclassification in a binary covariate in the random effect logistic model when a gold standard is not available. This Markov Chain Monte Carlo (MCMC) approach uses two imperfect measures of a dichotomous exposure under the assumptions of conditional independence and non-differential misclassification. A simulated numerical example and a real clinical example are given to illustrate the proposed approach. Our results suggest that the estimated log odds of inpatient care and the corresponding standard deviation are much larger in our proposed method compared with the models ignoring misclassification. Ignoring misclassification produces downwardly biased estimates and underestimate uncertainty.     

唐宋变革论体系的演化

自近代以来，由日本学者构筑的唐宋变革论体系，伴随时代的发展与新的知识资源的扩充，呈现出不断发展变化的趋势。本文旨在揭示这一演化的历史过程，尝试着分析这一过程变动发展的原因，并由此作出区别于以往的评价。     

Examples of the use of an equivalence theorem in constructing optimum experimental designs for random-effects nonlinear regression models

The problem considered is that of finding an optimum measurement schedule to estimate population parameters in a nonlinear model when the patient effects are random. The paper presents examples of the use of sensitivity functions, derived from the General Equivalence Theorem for D-optimality, in the construction of optimum population designs for such schedules. With independent observations, the theorem applies to the potential inclusion of a single observation. However, in population designs the observations are correlated and the theorem applies to the inclusion of an additional measurement schedule. In one example, three groups of patients of differing size are subject to distinct schedules. Numerical, as opposed to analytical, calculation of the sensitivity function is advocated. The required covariances of the observations are found by simulation.