简论语料库与教师角色之转变

现代电子语料库已有了近50年的发展,但语料库应用于课堂教学才刚刚起步。通过近年语料库辅助课堂教学诸多实践研究,教师们不仅可以获得教学中的例句,还可引导学生们学习更地道、更准确的表达方式。     

论金融危机下我国中小企业融资困难的成因及对策

金融危机下,中小企业融资难除了国际因素外,既有企业自身的原因,也有金融部门和地方政府等外在原因。解决中小企业融资难问题必须通过企业自身的努力和国家、金融部门、地方政府以及社会各方面的支持和帮助,才能得以实现。     

秦汉礼制中渗透的神学观念——以河北地区为例

秦汉时期,是宗教与封建专制融合的时代,以方术、巫术为基础的宗天神学和谶纬神学得到统治者推崇,对秦汉政治、文化产生了深刻的影响,甚至秦汉时期许多封建礼制和典章制度,都源于方术及其相关的宗教信仰。而河北地区的燕赵故地,正是神学宗教盛行的地区。以大量史实为基础,研究秦汉礼制中蕴含的神学宗教,可剖析封建礼制与宗教的关系和相互作用。     

“期刊承认”VS“共同体承认”:我国学术知识生产动力机制的“悖论”

共同体的承认是知识生产的根本动力,在西方期刊承认乃共同体承认的有机组成部分,学术人努力生产、发表创新性的知识以赢得共同体的承认.然而在我国,量化考评下的期刊承认并未得到大部分学术人的真正认同,已非共同体承认的一部分,学术人生产知识主要是为了取得形式化的期刊承认以满足各种实用性的目的,而非生产出创新性的知识以赢得实质性的共同体承认,期刊承认与共同体承认之间的冲突导致了我国学术知识生产动力机制的失灵.为了化解两者之间矛盾,我们一方面应使同行评议机制在各种学术评价活动中占据主导地位,另一方面要进一步完善学术期刊的匿名评审制度,以弥合期刊承认与共同体承认之间的断裂,从而真正激励学术知识的生产.     

不动产善意取得制度与公示公信制度的协调与适用

随着善意取得制度的发展,不动产善意取得制度在大陆法系国家陆续被确立,我国也在新的物权法中规定出不动产善意取得制度.但是我国存在着严格的公示公信制度,这个制度可以最大限度的解决不动产的纠纷,在这种情况下,不动产善意取得制度的存在必要性就受到质疑.文章通过对这两个制度的分析比较,指出不动产善意取得制度存在的必要性以及适用情况.     

Identifying the most successful dose (MSD) in dose-finding studies in cancer

For a dose finding study in cancer, the most successful dose (MSD), among a group of available doses, is that dose at which the overall success rate is the highest. This rate is the product of the rate of seeing non-toxicities together with the rate of tumor response. A successful dose finding trial in this context is one where we manage to identify the MSD in an efficient manner. In practice we may also need to consider algorithms for identifying the MSD which can incorporate certain restrictions, the most common restriction maintaining the estimated toxicity rate alone below some maximum rate. In this case the MSD may correspond to a different level than that for the unconstrained MSD and, in providing a final recommendation, it is important to underline that it is subject to the given constraint. We work with the approach described in O'Quigley et al. [Biometrics 2001; 57(4):1018-1029]. The focus of that work was dose finding in HIV where both information on toxicity and efficacy were almost immediately available. Recent cancer studies are beginning to fall under this same heading where, as before, toxicity can be quickly evaluated and, in addition, we can rely on biological markers or other measures of tumor response. Mindful of the particular context of cancer, our purpose here is to consider the methodology developed by O'Quigley et al. and its practical implementation. We also carry out a study on the doubly under-parameterized model, developed by O'Quigley et al. but not     

A BAYESIAN INTERPRETATION OF MULTIPLE POINT ESTIMATES

Consider a large number of econometric investigations using different estimation techniques and/or different subsets of all available data to estimate a fixed set of parameters. The resulting empirical distribution of point estimates can be shown - under suitable conditions - to coincide with a Bayesian posterior measure on the parameter space induced by a minimum information procedure. This Bayesian interpretation makes it easier to combine the results of various empirical exercises for statistical decision making. The collection of estimators may be generated by one investigator to ensure the satisfaction of our conditions, or they may be collected from published works, where behavioral assumptions need to be made regarding the dependence structure of econometric studies.     

Order statistics from non-identical right-truncated Lomax random variables with applications

In this paper, we derive some recurrence relations for the single and the product moments of order statistics from n independent and non-identically distributed Lomax and right-truncated Lomax random variables. These recurrence relations are simple in nature and could be used systematically in order to compute all the single and product moments of all order statistics in a simple recursive manner. The results for order statistics from the multiple-outlier model (with a slippage of p observations) are deduced as special cases. We then apply these results by examining the robustness of censored BLUE's to the presence of multiple outliers. Received: November 30, 1998; revised version: March 8, 2000     

Csiszar’s ϕ-divergences for testing the order in a Markov chain

x ₁, ..., x _n+r can be treated as the sample values of a Markov chain of order r or less (chain in which the dependence extends over r+1 consecutive variables only), and consider the problem of testing the hypothesis H ₀ that a chain of order r− 1 will be sufficient on the basis of the tools given by the Statistical Information Theory: _ϕ-Divergences. More precisely, if p _{a 1 ....., a r}: _{a r +1} denotes the transition probability for a r ^th order Markov chain, the hypothesis to be tested is H ₀:p _{a 1 ....., a r}: _{a r +1} = p _{a 2 ....., a r}: _{a r +1}, a _i ∈{1, ..., s}, i = 1, ..., r + 1 The tests given in this paper, for the first time, will have as a particular case the likelihood ratio test and the test based on the chi-squared statistic. Received: August 3, 1998; revised version: November 25, 1999     

Bayesian inference for generalized additive mixed models based on Markov random field priors

Most regression problems in practice require flexible semiparametric forms of the predictor for modelling the dependence of responses on covariates. Moreover, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation in longitudinal or spatial data. We present a unified approach for Bayesian inference via Markov chain Monte Carlo simulation in generalized additive and semiparametric mixed models. Different types of covariates, such as the usual covariates with fixed effects, metrical covariates with non-linear effects, unstructured random effects, trend and seasonal components in longitudinal data and spatial covariates, are all treated within the same general framework by assigning appropriate Markov random field priors with different forms and degrees of smoothness. We applied the approach in several case-studies and consulting cases, showing that the methods are also computationally feasible in problems with many covariates and large data sets. In this paper, we choose two typical applications.