网络环境下的学习模式探索

教育模式是教育观念、教育思想、教与学理论的集中体现,网络的出现为学习者提供了适应个性化需求的自主学习的机会,建立满足不同学生需要的网络学习模式,是网络教学的当务之急。     

福建省耕地流向及其与经济发展的关系

土地利用数量的净变化是土地双向变化的结果。在单一土地利用类型动态度模型基础上利用单一土地利用动态变化辅助方法:列表清单法和单一土地利用动态度双向模型,对福建省及各设区市耕地流向进行分析。并在此基础上,研究了福建省耕地的减少与经济发展的关系,提出福建省耕地保护的措施。     

"生态旅游"概念探微

选取国外 44个生态旅游定义作为研究对象 ,通过确认关键词、对关键词进行聚类分析 ,将定义内容归纳为 6大类 11组分 ,进而构建生态旅游概念模型。在检验模型理论上的可靠性和实践上的适应性后 ,依据模型提出自己的生态旅游定义     

基于马尔可夫链的产品市场保有量预测模型

售后服务机构为了准确地准备库存,都需要预测产品的市场保有量。基于马尔可夫链建立的状态概率矩阵模型,与抽样调查、时间序列法、回归分析法等方法相比,更能准确预测市场产品保有量。与传统产品相比较,更新换代速度快的电子产品,要预测其市场保有量,更需要利用状态概率矩阵模型。     

建筑节能激励政策的非对称博弈分析

本文探讨了建筑节能领域中政府与房地产商群体之间的非对称博弈问题,分析了该博弈所具有的模仿者动态模型、斯坦克尔伯格模型的特点,并构造了两者关于建筑节能激励政策的非对称博弈模型,最后对双方分别提出了博弈策略建议。     

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.     

Asymptotic bias in the linear mixed effects model under non-ignorable missing data mechanisms

Summary.  In longitudinal studies, missingness of data is often an unavoidable problem. Estimators from the linear mixed effects model assume that missing data are missing at random. However, estimators are biased when this assumption is not met. In the paper, theoretical results for the asymptotic bias are established under non-ignorable drop-out, drop-in and other missing data patterns. The asymptotic bias is large when the drop-out subjects have only one or no observation, especially for slope-related parameters of the linear mixed effects model. In the drop-in case, intercept-related parameter estimators show substantial asymptotic bias when subjects enter late in the study. Eight other missing data patterns are considered and these produce asymptotic biases of a variety of magnitudes.     

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.