Quantile dispersion graphs to compare the efficiencies of cluster randomized designs

The purpose of this article is to compare efficiencies of several cluster randomized designs using the method of quantile dispersion graphs (QDGs). A cluster randomized design is considered whenever subjects are randomized at a group level but analyzed at the individual level. A prior knowledge of the correlation existing between subjects within the same cluster is necessary to design these cluster randomized trials. Using the QDG approach, we are able to compare several cluster randomized designs without requiring any information on the intracluster correlation. For a given design, several quantiles of the power function, which are directly related to the effect size, are obtained for several effect sizes. The quantiles depend on the intracluster correlation present in the model. The dispersion of these quantiles over the space of the unknown intracluster correlation is determined, and then depicted by the QDGs. Two applications of the proposed methodology are presented.     

马克思主义哲学中国化的生存论阐释

马克思主义哲学中国化,需要从马克思主义哲学在生存论意义上展开的现代哲学人类学以及世界历史时代的新方向上进行理解。在信仰的无神论倾向、经验主义及自然主义的哲学样式、流动性的辩证思维、实践思维方式以及伦理价值观上,马克思主义与中国哲学传统都存在着一定的相通性。马克思主义哲学中国化不仅引导、而且也将融入中国文化传统的当代转化,转化的关键在于马克思主义共生主义信仰的内化,并形成能够积极影响人类的新的生存理念。     

新时期社会转型的中国经验——新时期社会转型与和谐社会构建问题战略层面上的比较价值

中国新时期社会转型与和谐社会构建问题,是中国经济社会发展进程中提出和探索的具有全局性、战略性的双重命题,是对我国现代社会发展规律的基本认识,也是对中国改革与发展进程的一种新的理解.这双重命题必须同时上升为执政党和政府的时代性理念,必须置于中国特色社会主义这一"核心主题"的主流分析框架之下;必须从同等程度意义上,上升为执政党和政府的战略性决策层面;改革开放30年的今天,迫切需要分析中国特色社会主义的现代社会转型与和谐社会构建的内在联系;必须从真理性与价值性之统一的角度解析中国特色的现代社会转型与和谐社会构建问题双重命题的同等程度范畴的现实价值.     

Transformed generalized linear models

The estimation of data transformation is very useful to yield response variables satisfying closely a normal linear model. Generalized linear models enable the fitting of models to a wide range of data types. These models are based on exponential dispersion models. We propose a new class of transformed generalized linear models to extend the Box and Cox models and the generalized linear models. We use the generalized linear model framework to fit these models and discuss maximum likelihood estimation and inference. We give a simple formula to estimate the parameter that index the transformation of the response variable for a subclass of models. We also give a simple formula to estimate the r$r$th moment of the original dependent variable. We explore the possibility of using these models to time series data to extend the generalized autoregressive moving average models discussed by Benjamin et al. [Generalized autoregressive moving average models. J. Amer. Statist. Assoc. 98, 214–223]. The usefulness of these models is illustrated in a simulation study and in applications to three real data sets.     

A new alternative to the standard F test for clustered data

The data collection process and the inherent population structure are the main causes for clustered data. The observations in a given cluster are correlated, and the magnitude of such correlation is often measured by the intra-cluster correlation coefficient. The intra-cluster correlation can lead to an inflated size of the standard F test in a linear model. In this paper, we propose a solution to this problem. Unlike previous adjustments, our method does not require estimation of the intra-class correlation, which is problematic especially when the number of clusters is small. Our simulation results show that the new method outperforms the existing methods.     

五四新文化运动与中国文化传统三题

发端于五四时期的现代新儒家之基本价值倾向是主张现代化的,因而它亦应归于"五四新文化运动"的范畴,所谓"五四新文化运动是全盘反传统"的论断是不成立的。中国文化现代开展的内在逻辑体现为从"外在冲击"到"内在转化",五四新文化运动构成了这一过程的的转折点,虽然它的批判和否定曾经对中国文化传统造成了一定的冲击,但它没有也不可能造成中国文化传统的断裂。就中国文化现代开展的整体进程而言,中国文化精神之主体性的挺立是一个不断得到加强的过程,中国文化传统受到了越来越多的重视。五四新文化运动所开启的文化格局进一步充实和丰富了中国文化的生命力,为中国文化传统面向21世纪的新开展增添了新的生机与活力。     

高校大学生教育管理模式转型方向及实践方式

教育管理是高校一项重要的工作,关系到高校教育的质量和效能,同时也决定了人才的成长和未来发展。在这种情况下,必须要注重高校大学生教育管理模式的打造,积极推进教育管理模式转型,从而为日常教育工作提供基础和保证。当前高校大学生教育管理模式中存在管理方式单一、管理内容陈旧、管理制度落后等问题,严重影响了高校教育工作的开展,不利于教育进步。文章以此为出发点进行研究,并为高校大学生教育管理实践提供思路指导。     

A new Bayesian wavelet thresholding estimator of nonparametric regression

The methods of estimation of nonparametric regression function are quite common in statistical application. In this paper, the new Bayesian wavelet thresholding estimation is considered. The new mixture prior distributions for the estimation of nonparametric regression function by applying wavelet transformation are investigated. The reversible jump algorithm to obtain the appropriate prior distributions and value of thresholding is used. The performance of the proposed estimator is assessed with simulated data from well-known test functions by comparing the convergence rate of the proposed estimator with respect to another by evaluating the average mean square error and standard deviations. Finally by applying the developed method, density function of galaxy data is estimated.     

The discrepancy of P-values and posterior probability: Two-sided hypothesis of variance of normal distribution with unknown mean

This article is concerned with the comparison of P-value and Bayesian measure in point null hypothesis for the variance of Normal distribution with unknown mean. First, using fixed prior for test parameter, the posterior probability is obtained and compared with the P-value when an appropriate prior is used for the mean parameter. In the second, lower bounds of the posterior probability of H₀ under a reasonable class of prior are compared with the P-value. It has been shown that even in the presence of nuisance parameters, these two approaches can lead to different results in the statistical inference.     

The Bayes rule of the variance parameter of the hierarchical normal and inverse gamma model under Stein's loss

For the variance parameter of the hierarchical normal and inverse gamma model, we analytically calculate the Bayes rule (estimator) with respect to a prior distribution IG (alpha, beta) under Stein's loss function. This estimator minimizes the posterior expected Stein's loss (PESL). We also analytically calculate the Bayes rule and the PESL under the squared error loss. Finally, the numerical simulations exemplify that the PESLs depend only on alpha and the number of observations. The Bayes rules and PESLs under Stein's loss are unanimously smaller than those under the squared error loss.