基于文献关键词统计分析的我国开放与远程教育研究现状探析

以中国知网（CNKI）为平台,利用共词分析方法,对2006年1月至2015年6月间公开发表在开放与远程教育领域期刊论文的关键词进行统计分析,结合SATI软件和UCINET软件进行数据处理,探讨高频关键词之间的内在联系,揭示我国开放与远程教育研究的主题结构和核心领域。结果表明：此间我国开放与远程教育领域主要存在七大研究主题;远程学习、网络课程、学习支持服务、学习者是该领域的研究核心;移动学习、内容分析、学习科学和远程学习者将成为未来的研究热点。     

基于Web的远程教学管理系统的设计及实现

远程教学管理系统是进行远程教学的重要保证,利用它可以合理地调配教学资源、组织教学活动、总结教学数据,并有效地对远程学习中心实行业务指导和监控,保证教与学过程管理的时效。本文结合北京邮电大学网络教育学院学生管理的需求,分析了远程教学管理系统的设计及实现方案,利用www上的应用系统,实现了跨平台的、灵活的、通用的远程教学管理系统。     

武汉市固定资产投资与经济增长关系的实证分析

固定资产投资是经济增长的一个重要因素,对经济增长具有直接的拉动作用,同时对投资的调节也成为国家宏观调控的一个重要手段。以武汉市为例,选取了年固定资产投资与国内生产总值的数据进行协整分析,并建立了误差修正模型,得出了二者之间存在长期均衡关系。     

Generalized Expected Utility, Heteroscedastic Error, and Path Dependence in Risky Choice

We evaluate the fit of several generalized expected utility models under homoscedasticity and three different heteroscedastic error structures for the data set first reported in Hey and Orme (1994). Standard chi-squared tests are used for nested tests, and both the Akaike (1973) information criterion and its consistent version (Hurvich and Tsai, 1989) are used for non-nested ranking of these models. A testing framework is developed that explicitly accounts for the path-dependent nature of the model selection problem. Not only does the selection of preference models depend on the error structure assumed, but the reverse is also true: the selection of the error structure depends on the preference structure assumed. An erratum to this article is available at .     

On the Critical Value Behaviour of Multiple Decision Procedures

In this paper we investigate the asymptotic critical value behaviour of certain multiple decision procedures as e.g. simultaneous confidence intervals and simultaneous as well as stepwise multiple test procedures. Supposing that n hypotheses or parameters of interest are under consideration we investigate the critical value behaviour when n increases. More specifically, we answer e.g. the question by which amount the lengths of confidence intervals increase when an additional parameter is added to the statistical analysis. Furthermore, critical values of different multiple decision procedures as for instance step-down and step-up procedures will be compared. Some general theoretic results are derived and applied for various distributions.     

Correlation in a Bayesian framework

The authors consider the correlation between two arbitrary functions of the data and a parameter when the parameter is regarded as a random variable with given prior distribution. They show how to compute such a correlation and use closed form expressions to assess the dependence between parameters and various classical or robust estimators thereof, as well as between p‐values and posterior probabilities of the null hypothesis in the one‐sided testing problem. Other applications involve the Dirichlet process and stationary Gaussian processes. Using this approach, the authors also derive a general nonparametric upper bound on Bayes risks.     

Importance of interpolation when constructing double-bootstrap confidence intervals

We show that, in the context of double-bootstrap confidence intervals, linear interpolation at the second level of the double bootstrap can reduce the simulation error component of coverage error by an order of magnitude. Intervals that are indistinguishable in terms of coverage error with theoretical, infinite simulation, double-bootstrap confidence intervals may be obtained at substantially less computational expense than by using the standard Monte Carlo approximation method. The intervals retain the simplicity of uniform bootstrap sampling and require no special analysis or computational techniques. Interpolation at the first level of the double bootstrap is shown to have a relatively minor effect on the simulation error.     

On Bartlett correction of empirical likelihood for regularly spaced spatial data

Bartlett correction constitutes one of the attractive features of empirical likelihood because it enables the construction of confidence regions for parameters with improved coverage probabilities. We study the Bartlett correction of spatial frequency domain empirical likelihood (SFDEL) based on general spectral estimating functions for regularly spaced spatial data. This general formulation can be applied to testing and estimation problems in spatial analysis, for example testing covariance isotropy, testing covariance separability as well as estimating the parameters of spatial covariance models. We show that the SFDEL is Bartlett correctable. In particular, the improvement in coverage accuracies of the Bartlett‐corrected confidence regions depends on the underlying spatial structures. The Canadian Journal of Statistics 47: 455–472; 2019 © 2019 Statistical Society of Canada     

Generalized estimator of population mean by using conventional and non-conventional measures in the presence of measurement errors

In survey research, it is assumed that reported response by the individual is correct. However, given the issues of prestige bias, self-respect, respondent's reported data often produces estimated values which are highly deviated from the true values. This causes measurement error (ME) to be present in the sample estimates. In this article, the estimation of population mean in the presence of measurement error using information on a single auxiliary variable is studied. A generalized estimator of population mean is proposed. The class of estimators is obtained by using some conventional and non-conventional measures. Simulation and numerical study is also conducted to assess the performance of estimators in the presence and absence of measurement error.