Effects of non-normality on the power function in a one-way random model

The effects of non-normality on type-I and type-II errors in a one-way random model are investigated for moderate departures from normality. It is found that the probabilities of both errors are more sensitive to the kurtosis of between group effects than that of within group effects.     

Unconstrained parametrizations for variance-covariance matrices

The estimation of variance-covariance matrices through optimization of an objective function, such as a log-likelihood function, is usually a difficult numerical problem. Since the estimates should be positive semi-definite matrices, we must use constrained optimization, or employ a parametrization that enforces this condition. We describe here five different parametrizations for variance-covariance matrices that ensure positive definiteness, thus leaving the estimation problem unconstrained. We compare the parametrizations based on their computational efficiency and statistical interpretability. The results described here are particularly useful in maximum likelihood and restricted maximum likelihood estimation in linear and non-linear mixed-effects models, but are also applicable to other areas of statistics.     

Generalized linear models with functional predictors

Summary. We present a technique for extending generalized linear models to the situation where some of the predictor variables are observations from a curve or function. The technique is particularly useful when only fragments of each curve have been observed. We demonstrate, on both simulated and real data sets, how this approach can be used to perform linear, logistic and censored regression with functional predictors. In addition, we show how functional principal components can be used to gain insight into the relationship between the response and functional predictors. Finally, we extend the methodology to apply generalized linear models and principal components to standard missing data problems.     

内幕交易民事责任及诉讼机制探讨

不同类型的内幕交易受害人取诉权的条件和方式不尽相同。内幕交易民事责任的各个构成要件都有其特定内涵。内幕交易民事诉讼机制由代表人诉讼和股东派生诉讼所构成。     

Slack-variable models versus Scheffé's mixture models

Slack-variable models are compared against Scheffé's polynomial model for mixture experiments. The notion of model equivalence and the use of various diagnostic measures provide effective tools in making such comparisons, particularly when the experimental region is highly constrained. It is demonstrated that the choice of the best fitting model, through variable selection, depends on which mixture component is selected as a slack variable, and on the size of the fitted model. In addition, the equivalence of two well-known representations of a complete mixture model is shown to be valid. Two numerical examples are presented.     

论“温州模式”中的人口分布效应——浙江省地级市经济块状集聚的人口空间布局效应

温州是我国外来务工人员最为集中的几个城市之一,但是由学理分析而得出的结论却是温州不仅不存在外来务工人员的就业空间,而且还要从其内部向外大量迁移人口。这种现实与理论的矛盾构成的“温州悖论”,其实可以通过“行政区经济”和“经济区经济”的分析框架而得到回归,由此也就说明了经济发展过程中块状经济对人口分布所具有的巨大效应。     

略论绑架罪的几个问题

绑架罪在主观上具有勒索财物或获取其他非法利益的目的,在客观上是由绑架行为和勒索财物或提出不法要求行为两方面组成的.新刑法规定绑架罪最低起点刑为10年和绝对死刑失之于严,应通过补充立法或修改刑法的立法方式重新规定.     

流言的流变:SARS舆情的传播学分析

无论是对于传媒管理、还是对于社会与政治变革而言,SARS危机事件中的流言传播现象都是一个难得的研究样本。舆论的进程及相关的跨学科调查表明:流言生成于主流传媒的缺位,平息于民众信息权力的恢复;在流言的最初阶段,存在着属于中产阶层的敏感人群,这意味着传播管理有可能采取更有针对性的对策;流言传播对于“道听途说”的明显的信道依赖,表明了流言在信息封锁之中更可能演变为谣言和谎言,更为重要的趋势是,新媒介在流言传播中的作用已日益显著。     

Functional Modelling and Classification of Longitudinal Data*

Abstract. We review and extend some statistical tools that have proved useful for analysing functional data. Functional data analysis primarily is designed for the analysis of random trajectories and infinite‐dimensional data, and there exists a need for the development of adequate statistical estimation and inference techniques. While this field is in flux, some methods have proven useful. These include warping methods, functional principal component analysis, and conditioning under Gaussian assumptions for the case of sparse data. The latter is a recent development that may provide a bridge between functional and more classical longitudinal data analysis. Besides presenting a brief review of functional principal components and functional regression, we develop some concepts for estimating functional principal component scores in the sparse situation. An extension of the so‐called generalized functional linear model to the case of sparse longitudinal predictors is proposed. This extension includes functional binary regression models for longitudinal data and is illustrated with data on primary biliary cirrhosis.     

Dynamic factor models

Summary Factor models can cope with many variables without running into scarce degrees of freedom problems often faced in a regression-based analysis. In this article we review recent work on dynamic factor models that have become popular in macroeconomic policy analysis and forecasting. By means of an empirical application we demonstrate that these models turn out to be usefu in investigating macroeconomic problems.