一般Gauss－Markov模型下一般可估函数的线性充分性和线性完全性

发展了Ｂａｋｓａｌａｒｙ和Ｄｒｙｇａｓ提出的一般Ｇａｕｓｓ－Ｍａｒｋｏｖ模型中线性充分性、最小充分性和完全性的概念，用Ｒａｏ的最小二乘统一理论，给出了这些概念的刻划定理。     

拓展服务功能,深入挖掘高校档案价值内涵

随着高校事业的发展和科技的进步,作为重要的档案资源信息库,高校档案的重要 性越来越明显。充分发挥其价值,强化服务功能成为当前高校档案工作者研究课题之一。文 章在分析高校档案特点和价值的基础上,重点阐述了高校档案馆增强档案意识,强化服务功 能,做好档案工作需要努力做到五个方面的转变。     

Residual analysis for spatial point processes (with discussion)

Summary.  We define residuals for point process models fitted to spatial point pattern data, and we propose diagnostic plots based on them. The residuals apply to any point process model that has a conditional intensity; the model may exhibit spatial heterogeneity, interpoint interaction and dependence on spatial covariates. Some existing ad hoc methods for model checking (quadrat counts, scan statistic, kernel smoothed intensity and Berman's diagnostic) are recovered as special cases. Diagnostic tools are developed systematically, by using an analogy between our spatial residuals and the usual residuals for (non-spatial) generalized linear models. The conditional intensity λ plays the role of the mean response. This makes it possible to adapt existing knowledge about model validation for generalized linear models to the spatial point process context, giving recommendations for diagnostic plots. A plot of smoothed residuals against spatial location, or against a spatial covariate, is effective in diagnosing spatial trend or co-variate effects. Q – Q -plots of the residuals are effective in diagnosing interpoint interaction.     

社会转型与人际关系结构的变化--由情感型人际关系结构向理性型人际关系结构的转化

人们结成一定的人际关系结构是由人的需要决定的 ,而人的需要又可以简单地分为情感需要和理性需要 ,与此相应 ,人际关系结构可以分为情感型人际关系和理性型人际关系。在中国长期的传统社会里 ,由于受传统社会结构的影响 ,人际关系结构明显表现为以情感为核心 ,重情不重理是传统中国人行为的普遍逻辑。但是 ,当前我国正处于社会转型时期 ,社会结构正发生着急剧的变化 ,人际交往中理性成分日益增加 ,人际关系结构表现出由情感型人际关系结构向理性型人际关系结构变化的趋势 ,我们必须对此有着清醒的认识。     

一类多元Gauss-Weierstrass算子线性组合的加权逼近

该文给出了一类多元Gauss-Weierstrass算子线性组合加Jacobi权在一致逼近下的正、逆定理和逼近阶的特征刻划     

潜在顾客与现实顾客的比较分析

市场是某种货物或服务的所有现实购买和潜在购买，因此，市场竞争的实质是对潜在顾客与现实顾客的争夺。将潜在顾客与现实顾客作以比较分析，具有十分重要的意义；而如何反潜在顾客转化为现实顾客，是现代企业进一步把市场做大的核心问题。本指出了潜在顾客与现实顾客对于现代企业生存与发展的重要影响，分析了导致潜在顾客产生与存在的原因，重点讨论了把潜在顾客转化为现实顾客的策略与关键环节，以期对处于激烈的市场竞争旋涡之中的企业能有所裨益。     

一类二阶变系数微分方程的解

通过变量变换 ,将变系数线性常微分方程化为常系数线性常微分方程 ,再利用常数变易法给出了一类二阶变系数非齐线性微分方程的通解。     

An individual measure of relative survival

Summary.  Relative survival techniques are used to compare survival experience in a study cohort with that expected if background population rates apply. The techniques are especially useful when cause-specific death information is not accurate or not available as they provide a measure of excess mortality in a group of patients with a certain disease. Whereas these methods are based on group comparisons, we present here a transformation approach which instead gives for each individual an outcome measure relative to the appropriate background population. The new outcome measure is easily interpreted and can be analysed by using standard survival analysis techniques. It provides additional information on relative survival and gives new options in regression analysis. For example, one can estimate the proportion of patients who survived longer than a given percentile of the respective general population or compare survival experience of individuals while accounting for the population differences. The regression models for the new outcome measure are different from existing models, thus providing new possibilities in analysing relative survival data. One distinctive feature of our approach is that we adjust for expected survival before modelling. The paper is motivated by a study into the survival of patients after acute myocardial infarction.     

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.     

广义孙子定理

给出了一次同余式组的解的计算公式.