Bayesian inference and model comparison for asymmetric smooth transition heteroskedastic models

Inference, quantile forecasting and model comparison for an asymmetric double smooth transition heteroskedastic model is investigated. A Bayesian framework in employed and an adaptive Markov chain Monte Carlo scheme is designed. A mixture prior is proposed that alleviates the usual identifiability problem as the speed of transition parameter tends to zero, and an informative prior for this parameter is suggested, that allows for reliable inference and a proper posterior, despite the non-integrability of the likelihood function. A formal Bayesian posterior model comparison procedure is employed to compare the proposed model with its two limiting cases: the double threshold GARCH and symmetric ARX GARCH models. The proposed methods are illustrated using both simulated and international stock market return series. Some illustrations of the advantages of an adaptive sampling scheme for these models are also provided. Finally, Bayesian forecasting methods are employed in a Value-at-Risk study of the international return series. The results generally favour the proposed smooth transition model and highlight explosive and smooth nonlinear behaviour in financial markets.     

A new mixture model for capture heterogeneity

Summary.  We propose a mixture of binomial and beta–binomial distributions for estimating the size of closed populations. The new mixture model is applied to several real capture–recapture data sets and is shown to provide a convenient, objective framework for model selection. The new model is compared with three alternative models in a simulation study, and the results shed light on the general performance of models in this area. The new model provides a robust flexible analysis, which automatically deals with small capture probabilities.     

高等院校教务管理系统考务管理模块开发与应用

高等学校教务管理系统是高等院校教育信息化的产物,可以大大提高教务管理工作效率和管理水平,快速、稳步地提高教学管理质量和水平.应用教务管理系统是教学管理模式创新的必然趋势,是提高教学管理工作效率的重要途径,为教务管理规范化提供了有力保障,是实现教育管理信息化的迫切需要.2003年沈阳农业大学引进大学综合教务管理系统并投入使用.运行初期仅仅利用综合教务系统的学籍查询、教学排课、学生选课,以及成绩管理与查询功能.2010年又升级和购进新版综合教务管理系统,充分利用了考务管理和成绩管理功能,更大程度地减轻了管理人员的工作量,提高了教学管理的工作效率.     

加强大学生政治思想教育工作的思考

加强对高等院校学生的政治思想工作,首先要了解大学生的思想特点和存在的主要问题。只有针对性强的思想教育工作,才能收到实效。本文从如何看待当代的大学生、做好高校政治思想工作的关键因素、搞好高校政治理论教学的建议三个方面进行了研究和分析。     

Modelling change in cognitive understanding with finite mixtures

Psychological theories often posit the existence of several different states. Individuals are viewed as belonging to one of the states at a given age, but with development pass to another state. A main problem in evaluating such theories is representing the transition from one state to another over age. A stochastic transition framework is proposed which should be useful in many different settings. The model is illustrated with data from a cognitive development task.     

Efficacy of programmes for the unemployed: discrete time modelling of duration data from a matched-comparison study

An evaluation is described of two UK Government programmes for the long-term unemployed in Great Britain, Employment Training and Employment Action, using discrete time hazard modelling of event histories. The study design employed a closely matched comparison group and carefully chosen control variables to minimize the effect of selection bias on conclusions. The effect of unobserved heterogeneity is investigated by using some standard random effect model formulations.     

A Bayesian method of sample size determination with practical applications

Summary.  The problem motivating the paper is the determination of sample size in clinical trials under normal likelihoods and at the substantive testing stage of a financial audit where normality is not an appropriate assumption. A combination of analytical and simulation-based techniques within the Bayesian framework is proposed. The framework accommodates two different prior distributions: one is the general purpose fitting prior distribution that is used in Bayesian analysis and the other is the expert subjective prior distribution, the sampling prior which is believed to generate the parameter values which in turn generate the data. We obtain many theoretical results and one key result is that typical non-informative prior distributions lead to very small sample sizes. In contrast, a very informative prior distribution may either lead to a very small or a very large sample size depending on the location of the centre of the prior distribution and the hypothesized value of the parameter. The methods that are developed are quite general and can be applied to other sample size determination problems. Some numerical illustrations which bring out many other aspects of the optimum sample size are given.     

EWB-电子线路设计与教学的优秀工具

介绍EWB(ElectronicsWorkbench即电子工作台 )软件对电子线路设计与教学的意义及其特点、功能和使用方法     

Nonparametric estimation of a change in defect intensity

Summary.  In an important class of problems involving mixture distributions, interest focuses on the mixture proportions, considering other possible parameters as nuisance parameters. We formulate a new variation on such problems that arose in a study on the link between the number of cells in a charge-coupled detector image sensor that turned defective because of cosmic radiation and the storage conditions of such sensors. In this variation, the component densities are bivariate, there are two classes and only a subset of the mixture proportions is of relevance. We propose a nonparametric method to estimate the relevant subset of the proportions and apply our method to the data in our study.     

A mixture model for random graphs

The Erdös–Rényi model of a network is simple and possesses many explicit expressions for average and asymptotic properties, but it does not fit well to real-world networks. The vertices of those networks are often structured in unknown classes (functionally related proteins or social communities) with different connectivity properties. The stochastic block structures model was proposed for this purpose in the context of social sciences, using a Bayesian approach. We consider the same model in a frequentest statistical framework. We give the degree distribution and the clustering coefficient associated with this model, a variational method to estimate its parameters and a model selection criterion to select the number of classes. This estimation procedure allows us to deal with large networks containing thousands of vertices. The method is used to uncover the modular structure of a network of enzymatic reactions.