客运专线运营对区域经济的影响

铁路客运专线的运营将提高干线的客货运输能力,降低社会运输总成本,带动高新产业发展,节约旅行时间,带动第三产业的发展等.本文建立了数学模型,以秦沈客运专线为例进行了定量分析,为客运专线的规划提供参考依据.     

Influence diagnostics and outlier tests for semiparametric mixed models

Summary. Semiparametric mixed models are useful in biometric and econometric applications, especially for longitudinal data. Maximum penalized likelihood estimators (MPLEs) have been shown to work well by Zhang and co-workers for both linear coefficients and nonparametric functions. This paper considers the role of influence diagnostics in the MPLE by extending the case deletion and subject deletion analysis of linear models to accommodate the inclusion of a nonparametric component. We focus on influence measures for the fixed effects and provide formulae that are analogous to those for simpler models and readily computable with the MPLE algorithm. We also establish an equivalence between the case or subject deletion model and a mean shift outlier model from which we derive tests for outliers. The influence diagnostics proposed are illustrated through a longitudinal hormone study on progesterone and a simulated example.     

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     

网络文化透视

互联网的发展催生出新的文化形态--网络文化.它以其开放性、实时交互性和受众主导等独特优势迅速地发展起来,作用于广大网民,尤其是青少年网民.网络文化缤纷复杂的内容和异域风格对社会转型过程中的社会主义文化建设构成挑战,是新世纪中国网络文化发展所要解决的重要课题.     

具有生存性的WDM网络性能分析

研究了在动态业务量时,可调谐光收发器和波长变换器对生存性WDM网络性能的影响。通过在NSFNET、CERNET、MESH-TORUS网络中的仿真,又从网络阻塞率方面研究了两者对WDM网络性能的影响。研究结果表明,在业务量为动态的生存性WDM网络中,可调谐光收发器和波长变换器对网络的性能都有显著的提高,但波长变换器对网络性能的改善要大于可调谐光收发器。     

高校图书馆馆员在网络环境下应具备的知识和能力

高校图书馆馆员在网络环境下应具备计算机、网络知识和应用技能 ,文献信息的管理、收集与处理的能力 ,较高的外语应用能力 ,爱岗敬业精神和较强的人际关系协调能力 ,过硬的心理素质和较强的公关能力 ,精深的专业知识、广博的科学文化知识和知识运用的能力。     

动物遗传育种系列课程双螺旋循环教学模式的建立与实践

根据动物遗传育种系列课程的结构、教学特点、本学科学生的层次性、学科发展和对教师多能性的要求 ,对该系列课程进行了教学内容、教学方式、教学手段的全面改革 ,构建了一套双螺旋循环教学模式 ,即教师与课程的螺旋循环链和学生层次与学习过程的螺旋循环链。这两条“教”、“学”互动的螺旋循环链有机结合 ,每完成一次循环教学过程 ,双螺旋上升一个螺距 ,教师的教学水平提高 ,学生的综合素质和学历提高一个层次。