Marginal models for the association structure of hierarchical binary responses

Clustered binary responses are often found in ecological studies. Data analysis may include modeling the marginal probability response. However, when the association is the main scientific focus, modeling the correlation structure between pairs of responses is the key part of the analysis. Second-order generalized estimating equations (GEE) are established in the literature. Some of them are more efficient in computational terms, especially facing large clusters. Alternating logistic regression (ALR) and orthogonalized residual (ORTH) GEE methods are presented and compared in this paper. Simulation results show a slightly superiority of ALR over ORTH. Marginal probabilities and odds ratios are also estimated and compared in a real ecological study involving a three-level hierarchical clustering. ALR and ORTH models are useful for modeling complex association structure with large cluster sizes.     

A new family of multivariate slash distributions

In this article, a new form of multivariate slash distribution is introduced and some statistical properties are derived. In order to illustrate the advantage of this distribution over the existing generalized multivariate slash distribution in the literature, it is applied to a real data set.     

揭橥理论视域：“国进民退”争论背后的意识形态迷思

“国进民退”争议热潮背后隐藏的是以前困扰着部分中国共产党人,至今仍然困扰着部分共产党人的意识形态认知误区,回顾中国共产党宏观经济政策史和经济思想史的变迁历程,深入剖析中国共产党在意识形态层面上对“公有”和“非公有”经济成分的内在二元冲突,可以见出中国共产党要以“执政党”的自身定位来重构“革命党”时期的意识形态。     

DMD系统的环形光栅的设计和制作

本文讨论了基于数字微镜系统(DMD)的环形光栅的设计和制作方法。利用DMD系统,我们可以制作出低成本、半径可调的环形光栅,为使其集成到微光机电系统提供了一种可能。通过实验,我们获得了最大外半径为42.7μm的环形光栅。     

论教育基本问题的“五元”性——当代教育改革与发展的哲学反思

本文基于教育实践领域的矛盾现象,做自下而上的哲学反思。聚焦人与物、人与人、人与己、人与神及知与行这五类古今中外教育领域的根源性问题,通过剖析各类教育问题共同基点的五元性,发挥教育哲学的批判功能,帮助人们树立一种与构建和谐社会相适应的新的教育价值观。     

Combined multiple testing of multivariate survival times by censored empirical likelihood

In each study testing the survival experience of one or more populations, one must not only choose an appropriate class of tests, but further an appropriate weight function. As the optimal choice depends on the true shape of the hazard ratio, one is often not capable of getting the best results with respect to a specific dataset. For the univariate case several methods were proposed to conquer this problem. However, most of the interesting datasets contain multivariate observations nowadays. In this work we propose a multivariate version of a method based on multiple constrained censored empirical likelihood where the constraints are formulated as linear functionals of the cumulative hazard functions. By considering the conditional hazards, we take the correlation between the components into account with the goal of obtaining a test that exhibits a high power irrespective of the shape of the hazard ratio under the alternative hypothesis.     

Bayesian sample‐size determination methods considering both worthwhileness and unpromisingness for exploratory two‐arm randomized clinical trials with binary endpoints

A randomized exploratory clinical trial comparing an experimental treatment with a control treatment on a binary endpoint is often conducted to make a go or no‐go decision. Such an exploratory trial needs to have an adequate sample size such that it will provide convincing evidence that the experimental treatment is either worthwhile or unpromising relative to the control treatment. In this paper, we propose three new sample‐size determination methods for an exploratory trial, which utilize the posterior probabilities calculated from predefined efficacy and inefficacy criteria leading to a declaration of the worthwhileness or unpromisingness of the experimental treatment. Simulation studies, including numerical investigation, showed that all three methods could declare the experimental treatment as worthwhile or unpromising with a high probability when the true response probability of the experimental treatment group is higher or lower, respectively, than that of the control treatment group.