医疗纠纷可仲裁性的法理思考

在医患矛盾时常发生的今天,探求解决医疗纠纷的机制已经成为必要。针对医疗纠纷的可仲裁性展开法理分析,通过对国外医疗纠纷仲裁机制的考察,分析医疗纠纷仲裁的特殊功能,论证其可仲裁性,显得尤其重要。在我国立法现状的基础上,提出我国医疗纠纷可仲裁性采用一般模式的建议,具有一定的现实意义。     

村治模式转型：由封闭性自治到开放性自治的演进

村民自治模式具有封闭性自治的特点。在现代化进程的强烈冲击下,农村社会已经发生历史性变迁,农村社会逐步走向开放性与异质性,村民自治赖以存在的经济与社会基础逐步瓦解。农村经济与社会变迁的既有现实决定了必须建立适应当前农村社会开放性、流动性社会要求的新型自治实践模式。由“开放性自治”到“封闭性自治”的转型是农村基层民主发展的演进逻辑。     

移动互联网商业模式探讨

随着互联网技术与移动通信技术的不断发展和融合,以及人们手持移动终端设备(如手机、PDA、MID等)的大量普及,移动互联网的应用已经是大势所趋。移动互联网给电信业带来新的发展机会,也为移动增值业务创造了全新的商业运作模式,即“终端+服务”一体化商业模式、“软件服务化”商业模式、广告商业模式、电信与广电双网运营商业模式、FON类商业模式、传统移动增值商业模式等六类。     

无学科界面的大学通识教育模式及其构建

遵循整体观和相对性理念,在无学科界面的科学与人文整合模式的基础上,进一步提出无学科面的自然融合型的大学通识教育模式和内容结构.该内容结构包括:广义自然中,各层次系统的认知、共性的概念、理论和规律,各层次系统共性规律和理论的比较、评价.它融知识精华、素质教育和创新教育于一体,充分反映了通识教育的真谛.     

城市地铁项目建设对沿线住宅价格的影响研究——以西安市地铁2号线为例

随着我国城市化进程的不断加快,大、中型城市的交通拥堵问题越来越严重,而传统的公共交通方式已经不能满足居民日益增长的出行需求。目前,发展以常规公交和地铁交通为主体的公共交通体系是解决交通拥堵问题的有效途径。同时,地铁交通方式的出现又是沿线住宅价格的助推器。文章以正在建设中的西安地铁2号线为实证研究对象,对沿线住宅价格的变化进行定性和定量分析。     

A copula-based Markov chain model for the analysis of binary longitudinal data

A fully parametric first-order autoregressive (AR(1)) model is proposed to analyse binary longitudinal data. By using a discretized version of a copula, the modelling approach allows one to construct separate models for the marginal response and for the dependence between adjacent responses. In particular, the transition model that is focused on discretizes the Gaussian copula in such a way that the marginal is a Bernoulli distribution. A probit link is used to take into account concomitant information in the behaviour of the underlying marginal distribution. Fixed and time-varying covariates can be included in the model. The method is simple and is a natural extension of the AR(1) model for Gaussian series. Since the approach put forward is likelihood-based, it allows interpretations and inferences to be made that are not possible with semi-parametric approaches such as those based on generalized estimating equations. Data from a study designed to reduce the exposure of children to the sun are used to illustrate the methods.     

Collapsibility for Directed Acyclic Graphs

Abstract.  Collapsibility means that the same statistical result of interest can be obtained before and after marginalization over some variables. In this paper, we discuss three kinds of collapsibility for directed acyclic graphs (DAGs): estimate collapsibility, conditional independence collapsibility and model collapsibility. Related to collapsibility, we discuss removability of variables from a DAG. We present conditions for these three different kinds of collapsibility and relationships among them. We give algorithms to find a minimum variable set containing a variable subset of interest onto which a statistical result is collapsible.     

Analysis of robust design experiments with time-dependent ordinal response characteristics: a quality improvement study from the horticulture industry

An approach to the analysis of time-dependent ordinal quality score data from robust design experiments is developed and applied to an experiment from commercial horticultural research, using concepts of product robustness and longevity that are familiar to analysts in engineering research. A two-stage analysis is used to develop models describing the effects of a number of experimental treatments on the rate of post-sales product quality decline. The first stage uses a polynomial function on a transformed scale to approximate the quality decline for an individual experimental unit using derived coefficients and the second stage uses a joint mean and dispersion model to investigate the effects of the experimental treatments on these derived coefficients. The approach, developed specifically for an application in horticulture, is exemplified with data from a trial testing ornamental plants that are subjected to a range of treatments during production and home-life. The results of the analysis show how a number of control and noise factors affect the rate of post-production quality decline. Although the model is used to analyse quality data from a trial on ornamental plants, the approach developed is expected to be more generally applicable to a wide range of other complex production systems.     

Bayesian partial likelihood approach for tied observations

The Bayesian analysis based on the partial likelihood for Cox's proportional hazards model is frequently used because of its simplicity. The Bayesian partial likelihood approach is often justified by showing that it approximates the full Bayesian posterior of the regression coefficients with a diffuse prior on the baseline hazard function. This, however, may not be appropriate when ties exist among uncensored observations. In that case, the full Bayesian and Bayesian partial likelihood posteriors can be much different. In this paper, we propose a new Bayesian partial likelihood approach for many tied observations and justify its use.     

Transformed generalized linear models

The estimation of data transformation is very useful to yield response variables satisfying closely a normal linear model. Generalized linear models enable the fitting of models to a wide range of data types. These models are based on exponential dispersion models. We propose a new class of transformed generalized linear models to extend the Box and Cox models and the generalized linear models. We use the generalized linear model framework to fit these models and discuss maximum likelihood estimation and inference. We give a simple formula to estimate the parameter that index the transformation of the response variable for a subclass of models. We also give a simple formula to estimate the r$r$th moment of the original dependent variable. We explore the possibility of using these models to time series data to extend the generalized autoregressive moving average models discussed by Benjamin et al. [Generalized autoregressive moving average models. J. Amer. Statist. Assoc. 98, 214–223]. The usefulness of these models is illustrated in a simulation study and in applications to three real data sets.