论魏晋南朝的忠孝实践——以阳翟褚氏为例

忠孝取舍孰先孰后在魏晋南朝时期有一个演变的过程,弃忠从孝是当时社会的趋势和需要。阳翟褚氏在这一时期屡屡背信弃义违背＂忠＂的道德却又信守家族＂孝＂的传统,是当时忠孝实践观的集中代表。     

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。     

福建省少数民族新农村建设模式的思考——基于3个畲族村的分析

本文以上金贝村、茶岗村和黎前村为研究对象,分析研究他们的建设模式,从中得出有益的启示:(1)贯彻落实国家民族政策,用足、用好各项优惠措施;(2)发展规划先行;(3)转变思想观念,加强自我发展能力;(4)走渐进式发展道路;(5)科技助力,加快农业产业化发展;(6)充分发挥农民的主体作用;(7)民族旅游带动型的建设模式要突出民族文化。     

对创新高校思想政治理论课实践环节的思考

思想政治理论课实践教学环节是理论课教学体系的重要部分,是提升教学效果的有效途径之一。目前,实践教学存在着思想认识上有偏差、缺乏规范有效的组织管理、缺少资金和基地保障、缺乏科学的考核和评价体系等诸多问题,需要通过转变观念、建立和完善组织管理机制、设立专项经费和实践基地、建立健全实践教学评价体系等途径,使理论课实践教学更具有可操作性和实效性。     

大学英语听力教学管见

大学生在国家四、六级统考中表现出来的薄弱环节是听力差。本文从听力过程、影响听力的因素和图式理论的角度对大学英语听力教学进行了探讨,提出了听前导入、听音训练、口头反馈的听力教学模式     

Measuring non-linear dependence for two random variables distributed along a curve

We propose new dependence measures for two real random variables not necessarily linearly related. Covariance and linear correlation are expressed in terms of principal components and are generalized for variables distributed along a curve. Properties of these measures are discussed. The new measures are estimated using principal curves and are computed for simulated and real data sets. Finally, we present several statistical applications for the new dependence measures.     

Regularization through variable selection and conditional MLE with application to classification in high dimensions

It is often the case that high-dimensional data consist of only a few informative components. Standard statistical modeling and estimation in such a situation is prone to inaccuracies due to overfitting, unless regularization methods are practiced. In the context of classification, we propose a class of regularization methods through shrinkage estimators. The shrinkage is based on variable selection coupled with conditional maximum likelihood. Using Stein's unbiased estimator of the risk, we derive an estimator for the optimal shrinkage method within a certain class. A comparison of the optimal shrinkage methods in a classification context, with the optimal shrinkage method when estimating a mean vector under a squared loss, is given. The latter problem is extensively studied, but it seems that the results of those studies are not completely relevant for classification. We demonstrate and examine our method on simulated data and compare it to feature annealed independence rule and Fisher's rule.     

Another look at Huber's estimator: A new minimax estimator in regression with stochastically bounded noise

Huber's estimator has had a long lasting impact, particularly on robust statistics. It is well known that under certain conditions, Huber's estimator is asymptotically minimax. A moderate generalization in rederiving Huber's estimator shows that Huber's estimator is not the only choice. We develop an alternative asymptotic minimax estimator and name it regression with stochastically bounded noise (RSBN). Simulations demonstrate that RSBN is slightly better in performance, although it is unclear how to justify such an improvement theoretically. We propose two numerical solutions: an iterative numerical solution, which is extremely easy to implement and is based on the proximal point method; and a solution by applying state-of-the-art nonlinear optimization software packages, e.g., SNOPT. Contribution: the generalization of the variational approach is interesting and should be useful in deriving other asymptotic minimax estimators in other problems.     

On the asymptotic distribution of Cook's distance in logistic regression models

It sometimes occurs that one or more components of the data exert a disproportionate influence on the model estimation. We need a reliable tool for identifying such troublesome cases in order to decide either eliminate from the sample, when the data collect was badly realized, or otherwise take care on the use of the model because the results could be affected by such components. Since a measure for detecting influential cases in linear regression setting was proposed by Cook [Detection of influential observations in linear regression, Technometrics 19 (1977), pp. 15–18.], apart from the same measure for other models, several new measures have been suggested as single-case diagnostics. For most of them some cutoff values have been recommended (see [D.A. Belsley, E. Kuh, and R.E. Welsch, Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, 2nd ed., John Wiley & Sons, New York, Chichester, Brisban, (2004).], for instance), however the lack of a quantile type cutoff for Cook's statistics has induced the analyst to deal only with index plots as worthy diagnostic tools. Focussed on logistic regression, the aim of this paper is to provide the asymptotic distribution of Cook's distance in order to look for a meaningful cutoff point for detecting influential and leverage observations.     

Cumulative correspondence analysis of ordered categorical data from industrial experiments

Most studies of quality improvement deal with ordered categorical data from industrial experiments. Accounting for the ordering of such data plays an important role in effectively determining the optimal factor level of combination. This paper utilizes the correspondence analysis to develop a procedure to improve the ordered categorical response in a multifactor state system based on Taguchi's statistic. Users may find the proposed procedure in this paper to be attractive because we suggest a simple and also popular statistical tool for graphically identifying the really important factors and determining the levels to improve process quality. A case study for optimizing the polysilicon deposition process in a very large-scale integrated circuit is provided to demonstrate the effectiveness of the proposed procedure.