‘q-Pareto-Scalar’ Two-Stage Extremization Model and its Reducibility to One-Stage Model

A two-stage sequential choice model is studied, the first stage being defined by q-Pareto multicriterial choice rule, and the second stage being defined by scalar extremization model. In this model, at the first stage the q-Pareto rule choses alternatives which are not only undominated in terms of Pareto comparison, but also includes into choice the alternatives which are dominated by no more than q alternatives. Since the choice set of the first-stage usually contains too many elements, obtained set is used as a presentation for the second stage constructed by a scalar extremization model. The properties of the model are studied as well as its representability to one-stage scalar extremization model.     

谈加强高职学生的职业能力培养

近年来高等职业技术教育发展迅猛，但人们对高等职业技术教育及其培养目标等缺乏系统的认识，存在一定程度的偏差。阐述高等职业技术教育的涵义、培养目标及战略地位，探索如何加强高职学生的职业能力，以适应社会发展的需要，促进经济社会的快速发展。     

Avoiding 'data snooping' in multilevel and mixed effects models

Summary.  Multilevel or mixed effects models are commonly applied to hierarchical data. The level 2 residuals, which are otherwise known as random effects, are often of both substantive and diagnostic interest. Substantively, they are frequently used for institutional comparisons or rankings. Diagnostically, they are used to assess the model assumptions at the group level. Inference on the level 2 residuals, however, typically does not account for 'data snooping', i.e. for the harmful effects of carrying out a multitude of hypothesis tests at the same time. We provide a very general framework that encompasses both of the following inference problems: inference on the 'absolute' level 2 residuals to determine which are significantly different from 0, and inference on any prespecified number of pairwise comparisons. Thus, the user has the choice of testing the comparisons of interest. As our methods are flexible with respect to the estimation method that is invoked, the user may choose the desired estimation method accordingly. We demonstrate the methods with the London education authority data, the wafer data and the National Educational Longitudinal Study data.     

单一经济目标下人类生存完整性的缺失与修复

单一经济目标的发展带来了人与自然、人与社会及人与人和谐关系的丧失,从而破坏了人类生存的完整性,造成了自然对人的惩罚、人的新异化的出现和经济发展的不可持续性。要修复人类生存的完整性,就必须改变单一经济目标,修复歪曲的价值等级序列,寻求经济的合理形态以及技术的生态化。     

建立和完善我国市场监管体系的思路、目标与措施

建立和完善与我国社会主义经济体制相适应的市场监管体系尤为重要。应力求突破现行体制,完善主体结构,健全制度体系,建立以政府宏观调控为基础,行政执法、行业自律、舆论监督、群众参与相结合;实施政府主导、多主体分层监管、社会广泛参与的监管模式,不断提高监管效能,保障市场安全有效运行;进一步促进社会主义市场经济的全面建设与发展。     

Likelihood-based estimation for Gaussian MRFs

Markov random fields (MRFs) express spatial dependence through conditional distributions, although their stochastic behavior is defined by their joint distribution. These joint distributions are typically difficult to obtain in closed form, the problem being a normalizing constant that is a function of unknown parameters. The Gaussian MRF (or conditional autoregressive model) is one case where the normalizing constant is available in closed form; however, when sample sizes are moderate to large (thousands to tens of thousands), and beyond, its computation can be problematic. Because the conditional autoregressive (CAR) model is often used for spatial-data modeling, we develop likelihood-inference methodology for this model in situations where the sample size is too large for its normalizing constant to be computed directly. In particular, we use simulation methodology to obtain maximum likelihood estimators of mean, variance, and spatial-depencence parameters (including their asymptotic variances and covariances) of CAR models.     

论网络教育游戏的设计

本文着重讨论如何设计既吸引青少年积极参与，又能得到学校家长教师肯定的网络教育游戏，提出任务驱动、有限目标、竞争合作、对象适应、功能完备等策略。     

大学英语"多元互补型"课堂教学模式

为更好地探究"多元互补型"课堂教学模式在西部地区地方性工科院校大学英语教学中的运用,首先指出"多元互补型"课堂教学模式是建立在一定的理论基础之上,接着阐述"多元互补型"课堂教学模式的具体构成,最后强调对大学英语"多元互补型"课堂教学模式的研究,旨在探索出适合于西部地区地方性工科院校大学英语教学的有效的教学模式,最终促进大学英语的教学改革.     

论电信普遍服务的实施模式与效益体现

电信普遍服务源自美国。经过近百年的发展，电信普遍服务业已成为世界大多数国家的共识。在广泛搜集大量文献资料的基础上，对电信普遍服务的定义、特征、对象、内容、分类、目标、原则、成本、补偿与效益等问题进行了概括与归纳，初步勾勒出了近年来电信普遍服务理论研究的基本态势。     

What can Statistics Contribute to a Causal Understanding?

Abstract.  We discuss the concept of causality in a broad manner. The traditional attitude in statistics has been to shy away from the causality concept. In recent years, however, a more proactive attitude to the causality concept has developed among statisticians. On the one hand, there is the school of counterfactual causality directed towards forming a better basis for clinical trials and epidemiology. On the other hand, there is the mechanistic view of causality. These developments are discussed and set into a larger context, where the often ignored role of time is emphasized. An extension of path analysis to stochastic processes is briefly presented. Causality is furthermore discussed in relation to genetic studies and to the emerging field of systems biology. Statisticians should also relate to the philosophical content of causality, especially that found in the foundations of physics.