On pooling data summaries in the absence of interactions “response-by-study”

The standard hypothesis testing procedure in meta-analysis (or multi-center clinical trials) in the absence of treatment-by-center interaction relies on approximating the null distribution of the standard test statistic by a standard normal distribution. For relatively small sample sizes, the standard procedure has been shown by various authors to have poor control of the type I error probability, leading to too many liberal decisions. In this article, two test procedures are proposed, which rely on thet—distribution as the reference distribution. A simulation study indicates that the proposed procedures attain significance levels closer to the nominal level compared with the standard procedure.     

On Block Updating in Markov Random Field Models for Disease Mapping

Gaussian Markov random field (GMRF) models are commonly used to model spatial correlation in disease mapping applications. For Bayesian inference by MCMC, so far mainly single-site updating algorithms have been considered. However, convergence and mixing properties of such algorithms can be extremely poor due to strong dependencies of parameters in the posterior distribution. In this paper, we propose various block sampling algorithms in order to improve the MCMC performance. The methodology is rather general, allows for non-standard full conditionals, and can be applied in a modular fashion in a large number of different scenarios. For illustration we consider three different applications: two formulations for spatial modelling of a single disease (with and without additional unstructured parameters respectively), and one formulation for the joint analysis of two diseases. The results indicate that the largest benefits are obtained if parameters and the corresponding hyperparameter are updated jointly in one large block. Implementation of such block algorithms is relatively easy using methods for fast sampling of Gaussian Markov random fields ( Rue, 2001 ). By comparison, Monte Carlo estimates based on single-site updating can be rather misleading, even for very long runs. Our results may have wider relevance for efficient MCMC simulation in hierarchical models with Markov random field components.     

Conditions for Non-confounding and Collapsibility without Knowledge of Completely Constructed Causal Diagrams

In this paper, we discuss several concepts in causal inference in terms of causal diagrams proposed by Pearl (1993 , 1995a , b ), and we give conditions for non-confounding, homogeneity and collapsibility for causal effects without knowledge of a completely constructed causal diagram. We first introduce the concepts of non-confounding, conditional non-confounding, uniform non-confounding, homogeneity, collapsibility and strong collapsibility for causal effects, then we present necessary and sufficient conditions for uniform non-confounding, homegeneity and collapsibilities, and finally we show sufficient conditions for non-confounding, conditional non-confounding and uniform non-confounding.     

区域发展潜力的评价方法及其应用

阐述了区域可持续发展的涵义 ,并从系统的角度对区域可持续发展进行了分析 ,提出了区域发展潜力的概念。构建了区域发展潜力的评价模型。最后对H市的发展潜力进行了评价     

多元化计量模式的现实思考--历史成本与公允价值择优并存

多元化计量模式的现实思考——历史成本与公允价值择优并存@谢桂芬$宁波高等专科学校经管系!浙江宁波315010~~     

血管的三维重建

构建了血管的三维重建模型 ,并给出了模型的算法及其求解结果 .     

存在品牌转移者和品牌忠诚者的产品差异模型

分析了存在品牌转移者和忠诚者两类特殊消费群情况下的两阶段水平差异模型，其中品牌转移者在选择产品时只考虑价格因素而不考虑运输成本，品牌忠诚者总是选择某一品牌的产品而不管价格和运输成本。讨论了在两阶段选址－定价博弈模型中纯战略均衡存在的条件。     

综合评判法在CAI课件评价中的应用

在CAI课件的开发过程中评价是一个十分重要的环节 .目前 ,对CAI课件的评价大多采用指标体系评定 ,然后对评价结果进行简单的加权 ,这样得到的结果往往受指标间的关联和主观等因素的影响 ,不能真实地反映课件质量 .为此 ,笔者给出了用模糊数学的综合评判法来评价CAI课件的方法 ,使对CAI课件的评价更科学、客观     

并行编程模式及分析

讨论了主从模型、单控制流多数据流、分治策略、数据流水线、投机策略、混合模型六种并行编程模型的结构和工作方式、性能及特点,分析统计了大量应用程序。指出了并行程序设计中,并行编程模式的选择直接影响了并行程序的正确性和效率,而主从模型和单控制流多数据流在整体性能上最优,程序执行效率也最好,是两种最常用的并行编程模型。     

面向素质教育的发展性学生评价

我国现行的学生评价制度与素质教育的要求还存在着一定的距离。为了促进素质教育取得突破性进展 ,我们必须倡导发展性评价观 ,建立起促进学生全面发展的学生评价体系。面向素质教育的发展性学生评价 ,其改革发展方向具体表现为 :评价功能全面化、评价标准多元化、评价过程动态化、评价方法多样化和评价主体互动化等方面