Approximations to the distribution of weighted combination of independent probabilities

Two simple approximations are proposed for the distribution of the weighted combina-tion of n independent probabilities. The approximations are compared with other avail-able approximations. It is shown that one of the proposed approximations is better than the other approximations.     

Variable selection via the weighted group lasso for factor analysis models

We consider the problem of selecting variables in factor analysis models. The $L_1$ regularization procedure is introduced to perform an automatic variable selection. In the factor analysis model, each variable is controlled by multiple factors when there are more than one underlying factor. We treat parameters corresponding to the multiple factors as grouped parameters, and then apply the group lasso. Furthermore, the weight of the group lasso penalty is modified to obtain appropriate estimates and improve the performance of variable selection. Crucial issues in this modeling procedure include the selection of the number of factors and a regularization parameter. Choosing these parameters can be viewed as a model selection and evaluation problem. We derive a model selection criterion for evaluating the factor analysis model via the weighted group lasso. Monte Carlo simulations are conducted to investigate the effectiveness of the proposed procedure. A real data example is also given to illustrate our procedure. The Canadian Journal of Statistics 40: 345–361; 2012 © 2012 Statistical Society of Canada     

Intentionally biased bootstrap methods

A class of weighted bootstrap techniques, called biased bootstrap or b-bootstrap methods, is introduced. It is motivated by the need to adjust empirical methods, such as the 'uniform' bootstrap, in a surgical way to alter some of their features while leaving others unchanged. Depending on the nature of the adjustment, the b-bootstrap can be used to reduce bias, or to reduce variance or to render some characteristic equal to a predetermined quantity. Examples of the last application include a b-bootstrap approach to hypothesis testing in nonparametric contexts, where the b-bootstrap enables simulation 'under the null hypothesis', even when the hypothesis is false, and a b-bootstrap competitor to Tibshirani's variance stabilization method. An example of the bias reduction application is adjustment of Nadaraya–Watson kernel estimators to make them competitive with local linear smoothing. Other applications include density estimation under constraints, outlier trimming, sensitivity analysis, skewness or kurtosis reduction and shrinkage.     

新闻评论专栏“冰点时评”大众性探析

在全球传媒领域迅速发展的今天,广大受众的参与意识日渐增强。新闻评论作为媒体和受众交流的平台及公众畅所欲言的阵地,其大众性越来越鲜明地体现出来,包括选材、立意、作者等诸多方面。新闻评论的大众性是我国新闻事业道路上的里程碑,突破了以前作家写稿、读者读稿的模式,使作者与读者之间的界限不再明显,越过了只闻一家之言的鸿沟。《冰点...     

儒学基督教对话:两种文化语境下的恩德观

恩德观在中西方民族中都是一个非常重要的文化概念。儒学倡导的知恩图报、感恩戴德等观念就蕴涵了这一传统文化精神。基督教所说的恩德,即上帝泽被苍生的恩爱之情,它具体表现为对人类的拯救和仁慈。从实质上讲,中西方恩德观强调的是施恩者与受恩者之间的伦理、宗教关系。就此而言,虽然儒学基督教两者承载的文化蕴意大相径庭,但却分享着人类面临的一些共同母题及思想前件。正是基于文化间相向而行的不同路径,才使得儒基之间的对话与融合、区分与碰撞成为可能。     

A study of sign adjustment in weighted signed networks

We analyse the adjustment of networks comprising of weighted positive (P) and negative (N) symmetric relations under the impact of various balancing rules. Five kinds of rules are studied: (1) a local minimal edge adjustment which is a special case of, (2) a local pressure based rule, (3) a local sign based rule, (4) a global rule and (5) rules varying on a local to global dimension. The convergence and convergent proportions of different 3-cycles and, thus the impact upon β(3) balance, under the different kinds of adjustment rule are studied both analytically and through simulation. The effects of network size (n), density (d) and the initial proportion of positive edges (α₀) upon the convergence of 3-cycles and, thus, balance and the eventual implications for the process of group formation are explored.     

经理才能、公司治理与契约参照点——中国上市公司高管薪酬决定因素的理论与实证分析

本文沿着中国企业激励制度市场化改革和全球公司治理趋同这两条主线,基于超越最优契约范式的市场配置视角、经理权力视角以及契约订立过程中的参照点效应,提出了关于中国上市公司高管薪酬决定因素的三组理论假设。基于502家上市公司2001至2006年平衡面板数据的实证研究显示,高管薪酬水平的上升反映了市场对经理才能日益增长的竞争性需求,但旨在约束经理权力、构架完备的公司治理机制并未发挥有效作用。此外,国际同行的薪酬基准对中国公司的高管薪酬决策具有参照点效应,这种薪酬设定过程中存在的参照点效应因薪酬委员会的存在而被放大。     

Different methods for handling incomplete longitudinal binary outcome due to missing at random dropout

This paper compares the performance of weighted generalized estimating equations (WGEEs), multiple imputation based on generalized estimating equations (MI-GEEs) and generalized linear mixed models (GLMMs) for analyzing incomplete longitudinal binary data when the underlying study is subject to dropout. The paper aims to explore the performance of the above methods in terms of handling dropouts that are missing at random (MAR). The methods are compared on simulated data. The longitudinal binary data are generated from a logistic regression model, under different sample sizes. The incomplete data are created for three different dropout rates. The methods are evaluated in terms of bias, precision and mean square error in case where data are subject to MAR dropout. In conclusion, across the simulations performed, the MI-GEE method performed better in both small and large sample sizes. Evidently, this should not be seen as formal and definitive proof, but adds to the body of knowledge about the methods’ relative performance. In addition, the methods are compared using data from a randomized clinical trial.     

政府与市场的关系:经济法学研究的逻辑起点

纵现市场经济的发展历程，并非完全是一个自由发展的过程，而是始终伴随着政府的干预和调节。正是政府干预和市场调节的对立统一才推动了专门的经济法律、法规的产生和发展。可见，专门的经济法学只有在近代资本主义产生之后才是可能的，政府和市场的关系是经济法学研究的一个逻辑起点，通过对政府与市场关系的深入研究，将使我们在经济法学和经济立法方面的许多理论困惑获得全新的认识和解决。     

Robust estimation in certain heteroscedastic linear models when there are many parameters

We study estimation of regression parameters in heteroscedastic linear models when the number of parameters is large. The results generalize work of Huber (1973), Yohai and Maronna (1979), and Carroll and Rupert (1982a).