简述《新华文摘》的学术文献价值

《新华文摘》与改革开放同步,以四项基本原则和“双百”方针为办刊宗旨,具有极高的思想价值:《新华文摘》融汇了浩大的人文社会科学和自然科学的信息量,具有极高的知识价值;《新华文摘》哺育了一代学人,具有极高的学术价值;《新华文摘》摘英撷华,汇集了许多传世之作,具有极高的文献收藏价值。     

双序结合整体教育改革及其本质特征

循序渐进原则是有效实施教育和教学的最根本原则。教育　改　革要从这一根本原则出发，从儿童心理发展序列和学科知识逻辑序列的最佳结合入手，从　整体着眼，全面改革中小学的教育和教学。双序结合整体教育改革的本质特征可概括为：系　统整体性；适时教育性；高速有效性；最优发展性。     

浅议高校辅导员的素质建设

高校辅导员在大学生全面发展中起主导作用,对大学生的成长有着直接的影响。充分重视新时期高校辅导员的素质建设,对深化教育改革,全面提高大学生的素质,培养符合社会需要的人才有重要意义。     

A fast splitting procedure for classification trees

This paper provides a faster method to find the best split at each node when using the CART methodology. The predictability index is proposed as a splitting rule for growing the same classification tree as CART does when using the Gini index of heterogeneity as an impurity measure. A theorem is introduced to show a new property of the index : the for a given predictor has a value not lower than the for any split generated by the predictor. This property is used to make a substantial saving in the time required to generate a classification tree. Three simulation studies are presented in order to show the computational gain in terms of both the number of splits analysed at each node and the CPU time. The proposed splitting algorithm can prove computational efficiency in real data sets as shown in an example.     

NONPARAMETRIC TWO SAMPLE TESTS BASED ON AREA STATISTICS

ABSTRACT

Area statistics are sample versions of areas occurring in a probability plot of two distribution functions F and G. This paper presents a unified basis for five statistics of this type. They can be used for various testing problems in the framework of the two sample problem for independent observations, such as testing equality of distributions against inequality or testing stochastic dominance of distributions in one or either direction against nondominance. Though three of the statistics considered have already been suggested in literature, two of them are new and deserve our interest. The finite sample distributions of the statistics (under F=G) can be calculated via recursion formulae. Two tables with critical values of the new statistics are included. The asymptotic distribution of the properly normalized versions of the area statistics are functionals of the Brownian bridge. The distribution functions and quantiles thereof are obtained by Monte Carlo simulation. Finally, the power functions of the two new tests based on area statistics are compared to the power functions of the tests based on the corresponding supremum statistics, i.e., statistics of the Kolmogorov–Smirnov type.     

元大都戏剧艺人略论

元大都辉煌的戏剧文化是杂剧家和戏剧艺人共同创造的。大都的戏剧艺人的演艺水平很高,一大批演员都具有角色行当上的专长。在大都梨园中不乏夫妻或父母子女同为艺人的,由此形成了一些艺人之家或艺人世家,元代戏剧艺人演艺水平之高,与此种现象有内在联系。此外,大都一些戏剧艺人在文学创作上也有令人瞩目的成绩。     

Correlation in a Bayesian framework

The authors consider the correlation between two arbitrary functions of the data and a parameter when the parameter is regarded as a random variable with given prior distribution. They show how to compute such a correlation and use closed form expressions to assess the dependence between parameters and various classical or robust estimators thereof, as well as between p‐values and posterior probabilities of the null hypothesis in the one‐sided testing problem. Other applications involve the Dirichlet process and stationary Gaussian processes. Using this approach, the authors also derive a general nonparametric upper bound on Bayes risks.     

Covariance-regularized regression and classification for high dimensional problems

Summary.  We propose covariance-regularized regression, a family of methods for prediction in high dimensional settings that uses a shrunken estimate of the inverse covariance matrix of the features to achieve superior prediction. An estimate of the inverse covariance matrix is obtained by maximizing the log-likelihood of the data, under a multivariate normal model, subject to a penalty; it is then used to estimate coefficients for the regression of the response onto the features. We show that ridge regression, the lasso and the elastic net are special cases of covariance-regularized regression, and we demonstrate that certain previously unexplored forms of covariance-regularized regression can outperform existing methods in a range of situations. The covariance-regularized regression framework is extended to generalized linear models and linear discriminant analysis, and is used to analyse gene expression data sets with multiple class and survival outcomes.