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。     

促进高校机关党建工作科学发展的若干思考

目前,高校机关党建工作在工作思路、制度建设及工作方法等方面存在不足,只有解放思想、与时俱进加强学习型党组织建设,才能开拓工作思路和提高工作水平;只有不断创新党建工作的方式方法、完善和改进制度建设,才能切实增强高校机关党组织的创造力、凝聚力、战斗力;只有全面带动和加强机关作风建设,机关党组织才能更好地为高校的教学、科研服务。     

维经斯基来华与马克思主义在中国的早期传播

长久以来,史学界对于维经斯基首次来华的研究资料比较匮乏,而作为共产国际和俄共（布）远东局派往中国的第一位代表,维经斯基在促进马克思主义传播、增强共产国际与中国先进知识分子之间的联系、促进中国共产党的创建等方面的作用不容忽视,甚至可以说是在中国有组织、有计划、有针对性地进行马克思主义传播的第一人。     

开好邓小平理论课的几点思考

邓小平理论课是高校马克思主义理论新课程体系的重点课。要开好这门课,必须抓好几个环节：一、抓师资建设;二、备好课;三、运用适宜的教学方法;四,开好第二课堂;五、完善考核办法     

对跨世纪广州师范学院教师队伍建设的若干看法

本文论述跨世纪教师队伍建设的重大战略意义并针对广州师范学院的某些实际情况,提出抓好跨世纪教师队伍建设的若干对策。     

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.