新时期高等学校教师培训工作的思考

分析了高等学校教师培训工作的现状,即不完全适应时代发展的需要;不完全适应我国加入WTO后高等教育与国际接轨的需要;不完全适应高等教育自身深化改革和发展的需要;不完全适应教师参训的个性愿望的需要。针对高等学校教师培训实际工作中存在重学历培训,轻师德培养;对教师创新能力、实践能力的培养不够重视;经费短缺,内容陈旧,考核机制不够完善等问题,提出了健全规章,实行依法培训;加强师德建设,采取多种形式,提高教师综合素质;设立专项培训经费及建立健全教师培训的激励机制的对策。     

论WTO框架下技术性贸易壁垒对我国对外贸易的影响及对策

文章分析了我国入世后,在关税壁垒和许多非关税壁垒对我国外贸发展的影响日渐减弱的情况下,WTO框架下的技术性贸易壁垒对我国外贸的不利影响.并提出四项对策:提高国内对技术性贸易壁垒的认识和了解,使企业重视技术性贸易壁垒的不断变化;将技术性贸易壁垒转化为标准化的推进器,提高产品质量;强化知识产权保护,使企业能不断提高自身的技术水平;实施"走出去"策略,规避繁苛的技术贸易壁垒,以期对我国外贸实践有所助益.     

大学计算机专业“基础原理”课的教学探讨

本文针对现代大学生学习计算机专业“基础原理”课程存在的种种客观问题,通过分析这些问题找出其中原因,并介绍对这些问题所采取的解决方案。教学实践表明,这些方案对学生学习“基础原理课”起到了较好的效果,提高了这类课程的教学质量。     

创新意识和思维方法论

美国著名经济学家熊彼特曾说,经济增长并不是由于资本、劳力等生产要素的增加所引起的植物性增长,而是创新所引起的增长。20世纪以来的100年,不仅在科学技术方面是一个创新的时代,同时也是社会变革最为迅速的时代。科学技术创新是社会发展的促进剂,社会创新同样对社会、经济发展产生深刻的影响。为此必须研究创新和创新思维方法的诸多方面,不断地用智慧去提出问题和解决问题,从而不断地产生新的思维方式和框架。在自由的思考之中,我们才最有可能产生创新。     

Generalized additive models for location, scale and shape

Summary.  A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects. The distribution for the response variable in the GAMLSS can be selected from a very general family of distributions including highly skew or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only of the mean (or location) but also of the other parameters of the distribution of y , as parametric and/or additive nonparametric (smooth) functions of explanatory variables and/or random-effects terms. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models. A Newton–Raphson or Fisher scoring algorithm is used to maximize the (penalized) likelihood. The additive terms in the model are fitted by using a backfitting algorithm. Censored data are easily incorporated into the framework. Five data sets from different fields of application are analysed to emphasize the generality of the GAMLSS class of models.     

Marginal and association regression models for longitudinal binary data with drop‐outs: A likelihood‐based approach

Longitudinal data often contain missing observations, and it is in general difficult to justify particular missing data mechanisms, whether random or not, that may be hard to distinguish. The authors describe a likelihood‐based approach to estimating both the mean response and association parameters for longitudinal binary data with drop‐outs. They specify marginal and dependence structures as regression models which link the responses to the covariates. They illustrate their approach using a data set from the Waterloo Smoking Prevention Project They also report the results of simulation studies carried out to assess the performance of their technique under various circumstances.     

Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods

Summary.  A stochastic discrete time version of the susceptible–infected–recovered model for infectious diseases is developed. Disease is transmitted within and between communities when infected and susceptible individuals interact. Markov chain Monte Carlo methods are used to make inference about these unobserved populations and the unknown parameters of interest. The algorithm is designed specifically for modelling time series of reported measles cases although it can be adapted for other infectious diseases with permanent immunity. The application to observed measles incidence series motivates extensions to incorporate age structure as well as spatial epidemic coupling between communities.     

Social identities and political cleavages: the role of political context

Summary. Using a novel method, the paper investigates the influence of social group identities on attitudes and on voting in a variety of political contexts. Examining the major regions of Britain, Canada and the USA, we find considerable national and regional diversity in the nature of social cleavages. For example, social class and race had widely different effects across societies, but within societies their effects on attitudes and on voting were very similar. However, despite that, age and religion had a similar effect on attitudes across societies; the effects on voting varied considerably. The significant within-country differences underline the importance of using region, rather than country, as the unit of analysis. More importantly, these results highlight the role of political context, especially competing cleavages and the structure of party competition, in the establishment of politically relevant social cleavages.     

Detection and correction of artificial shifts in climate series

Summary.  Many long instrumental climate records are available and might provide useful information in climate research. These series are usually affected by artificial shifts, due to changes in the conditions of measurement and various kinds of spurious data. A comparison with surrounding weather-stations by means of a suitable two-factor model allows us to check the reliability of the series. An adapted penalized log-likelihood procedure is used to detect an unknown number of breaks and outliers. An example concerning temperature series from France confirms that a systematic comparison of the series together is valuable and allows us to correct the data even when no reliable series can be taken as a reference.     

A note on interval‐censored lifetime data and the constant‐sum condition of oiler,gómez & calle (2004)

Oiler, Gomez & Calle (2004) give a constant sum condition for processes that generate interval‐censored lifetime data. They show that in models satisfying this condition, it is possible to estimate non‐parametrically the lifetime distribution based on a well‐known simplified likelihood. The author shows that this constant‐sum condition is equivalent to the existence of an observation process that is independent of lifetimes and which gives the same probability distribution for the observed data as the underlying true process.