The analysis of incomplete data using stochastic covariates

In the development of many diseases there are often associated random variables which continuously reflect the progress of a subject towards the final expression of the disease (failure). At any given time these processes, which we call stochastic covariates, may provide information about the current hazard and the remaining time to failure. Likewise, in situations when the specific times of key prior events are not known, such as the time of onset of an occult tumour or the time of infection with HIV-1, it may be possible to identify a stochastic covariate which reveals, indirectly, when the event of interest occurred. The analysis of carcinogenicity trials which involve occult tumours is usually based on the time of death or sacrifice and an indicator of tumour presence for each animal in the experiment. However, the size of an occult tumour observed at the endpoint represents data concerning tumour development which may convey additional information concerning both the tumour incidence rate and the rate of death to which tumour-bearing animals are subject. We develop a stochastic model for tumour growth and suggest different ways in which the effect of this growth on the hazard of failure might be modelled. Using a combined model for tumour growth and additive competing risks of death, we show that if this tumour size information is used, assumptions concerning tumour lethality, the context of observation or multiple sacrifice times are no longer necessary in order to estimate the tumour incidence rate. Parametric estimation based on the method of maximum likelihood is outlined and is applied to simulated data from the combined model. The results of this limited study confirm that use of the stochastic covariate tumour size results in more precise estimation of the incidence rate for occult tumours.     

Modified cross-validation in density estimation

We propose a new modified (biased) cross-validation method for adaptively determining the bandwidth in a nonparametric density estimation setup. It is shown that the method provides consistent minimizers. Some simulation results are reported on which compare the small sample behavior of the new and the classical cross-validation selectors.     

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。     

论地方综合大学外语教学三维一体管理的模式

根据地方综合性大学的特点,确立外语教师、学生、辅导员三维一体的教学管理模式具有它的可行性。在这个模式中,教师是教学系统稳态的维系力量,学生是外语教学的主体,辅导员对学生外语学习活动起着导向作用。其三者之间是缺一不可的良性互动的辩证关系。     

论西部地区地方政府在构建和谐社会中的主导作用

西部地区构建和谐社会是一个长期的、逐渐的过程.地方政府作为构建和谐社会的主体,承担着更为繁重的任务,尤其是经济欠发达的西部地区.构建社会主义和谐社会,地方政府必须调动一切积极因素,增强全社会的创造活力;发挥引导、协调和支持的作用,广泛调动社会各方面的积极性,调整好各方面的利益关系,维护和实现社会公平;营造良好的社会氛围,形成和谐相处的人际环境;加强社会建设和管理,切实维护社会稳定.必须深化各项改革,形成一个促进地区经济社会协调发展的体制机制,为构建社会主义和谐社会提供有力的体制保障.     

民族地区县级政府竞争：路径选择与制度安排

我国地方政府竞争的趋势将愈演愈烈,地方政府竞争带来了不同的竞争效应。文章对地方政府竞争模型的反思入手,分析了民族地区政府竞争的特征和存在问题的基础上,提出了民族地区提升政府竞争力的路径选择:以提升制度竞争力为先,推进市场、产品等要素建设;并对民族地区政府竞争提出相应的制度和政策安排。     

西部诗人高凯的乡土世界

甘肃诗人高凯的乡土诗在中国西部当代诗歌中是独特的,他构筑的乡土世界已逐渐引起评论家的关注并赢得较高的评价。他的乡土诗主要有这样三部分组成:一,风俗画的展现;二,乡情、乡愁的吟唱;三,家园意识的追寻。高凯的乡土世界是厚重的、完整的。诗歌具有哲学和人类学的思想高度。     

Local dependence estimation using semiparametric archimedean copulas

The authors define a new semiparametric Archimedean copula family which has a flexible dependence structure. The generator of the family is a local interpolation of existing generators. It has locally‐defined dependence parameters. The authors present a penalized constrained least‐squares method to estimate and smooth these parameters. They illustrate the flexibility of their dependence model in a bi‐variate survival example.     

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.     

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