论增强党的阶级基础和扩大党的群众基础

论述了坚持党的阶级基础和扩大党的群众基础是马克思主义建党学说十分注重的问题 ,阐明了不断增强党的阶级基础和不断扩大党的群众基础是中国共产党发展建设的成功经验 ,分析了新时期不断增强党的阶级基础和不断扩大党的群众基础是时代和形势发展变化的客观要求 ,提出了新世纪不断增强党的阶级基础和不断扩大党的群众基础应该注意把握的两个基本问题。     

英语写作中有效反馈信息的探讨

文章简要阐述了反馈信息,论述了作文工作研究的意义及在作文写作中有效的信息反馈形式,提出了增强写作能力的可行措施。     

整体性理论与语文阅读教学

格式塔关于学习的整体性理论，为语文整体阅读教学模式提供理论依据，证明语文整体阅读的科学性和可行性。语文阅读教学在教学内容、教学结构、教学方法和教学策略等方面实践整体性阅读，将阅读教学的基点从肢解教材的概念化分析转移到整体感知和整体把握上，有利于提高阅读教学的质量和效率。     

论大学生的阅读倾向及图书馆的服务对策

当前大学生的阅读倾向具有求知、求识、求美、求乐等特点，图书馆应及时了解和把握这些特点，加强对读阅读倾向的调研工作，提高服务针对性；在献资源建设上既要统筹兼顾，又要突出重点；要加快图书馆现代化建设，提高为读服务的水平，还要做好导读工作，尤其做好书目推荐工作。     

Estimating household structure in ancient China by using historical data: a latent class analysis of partially missing patterns

Summary.  Social data often contain missing information. The problem is inevitably severe when analysing historical data. Conventionally, researchers analyse complete records only. Listwise deletion not only reduces the effective sample size but also may result in biased estimation, depending on the missingness mechanism. We analyse household types by using population registers from ancient China (618–907 AD) by comparing a simple classification, a latent class model of the complete data and a latent class model of the complete and partially missing data assuming four types of ignorable and non-ignorable missingness mechanisms. The findings show that either a frequency classification or a latent class analysis using the complete records only yielded biased estimates and incorrect conclusions in the presence of partially missing data of a non-ignorable mechanism. Although simply assuming ignorable or non-ignorable missing data produced consistently similarly higher estimates of the proportion of complex households, a specification of the relationship between the latent variable and the degree of missingness by a row effect uniform association model helped to capture the missingness mechanism better and improved the model fit.     

Interpreting DNA Mixtures with Related Contributors in Subdivided Populations

Abstract.  In this paper, we study the statistical interpretation of forensic DNA mixtures with related contributors in subdivided populations. Compact general formulae for match probabilities are obtained for two situations: a relative of one tested person is an unknown contributor of a DNA mixture; and two related unknowns are contributors. The effect of kinship and population structure is illustrated using a real case example.     

Choice of the primary analysis in longitudinal clinical trials

Missing data, and the bias they can cause, are an almost ever‐present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood‐based, mixed‐effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF ANOVA as the primary analysis and focus on likelihood‐based, mixed‐effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright © 2004 John Wiley & Sons, Ltd.     

浅谈高校英语教师所应具备的素质

精读课是英语专业学生的一门主修课.精读教师的素质对教学质量以及大学生素质的培养起着关键性的作用.本文从精度课角度简单分析了高校英语教师应具备的素质--能力素质和思想道德素质.     

Creating a reference to a complex emergency situation using time series methods: war in Guinea-Bissau 1998-1999

Impacts of complex emergencies or relief interventions have often been evaluated by absolute mortality compared to international standardized mortality rates. A better evaluation would be to compare with local baseline mortality of the affected populations. A projection of population-based survival data into time of emergency or intervention based on information from before the emergency may create a local baseline reference. We find a log-transformed Gaussian time series model where standard errors of the estimated rates are included in the variance to have the best forecasting capacity. However, if time-at-risk during the forecasted period is known then forecasting might be done using a Poisson time series model with overdispersion. Whatever, the standard error of the estimated rates must be included in the variance of the model either in an additive form in a Gaussian model or in a multiplicative form by overdispersion in a Poisson model. Data on which the forecasting is based must be modelled carefully concerning not only calendar-time trends but also periods with excessive frequency of events (epidemics) and seasonal variations to eliminate residual autocorrelation and to make a proper reference for comparison, reflecting changes over time during the emergency. Hence, when modelled properly it is possible to predict a reference to an emergency-affected population based on local conditions. We predicted childhood mortality during the war in Guinea-Bissau 1998-1999. We found an increased mortality in the first half-year of the war and a mortality corresponding to the expected one in the last half-year of the war.