Robust tests for the common principal components model

When dealing with several populations, the common principal components (CPC) model assumes equal principal axes but different variances along them. In this paper, a robust log-likelihood ratio statistic allowing to test the null hypothesis of a CPC model versus no restrictions on the scatter matrices is introduced. The proposal plugs into the classical log-likelihood ratio statistic robust scatter estimators. Using the same idea, a robust log-likelihood ratio and a robust Wald-type statistic for testing proportionality against a CPC model are considered. Their asymptotic distributions under the null hypothesis and their partial influence functions are derived. A small simulation study allows to compare the behavior of the classical and robust tests, under normal and contaminated data.     

Nonparametric control charts based on runs and Wilcoxon-type rank-sum statistics

In this article, we introduce three new distribution-free Shewhart-type control charts that exploit run and Wilcoxon-type rank-sum statistics to detect possible shifts of a monitored process. Exact formulae for the alarm rate, the run length distribution, and the average run length (ARL) are all derived. A key advantage of these charts is that, due to their nonparametric nature, the false alarm rate (FAR) and in-control run length distribution is the same for all continuous process distributions. Tables are provided for the implementation of the charts for some typical FAR values. Furthermore, a numerical study carried out reveals that the new charts are quite flexible and efficient in detecting shifts to Lehmann-type out-of-control situations.     

Multivariate regression analysis of panel data with binary outcomes applied to unemployment data

Summary  In panel studies binary outcome measures together with time stationary and time varying explanatory variables are collected over time on the same individual. Therefore, a regression analysis for this type of data must allow for the correlation among the outcomes of an individual. The multivariate probit model of Ashford and Sowden (1970) was the first regression model for multivariate binary responses. However, a likelihood analysis of the multivariate probit model with general correlation structure for higher dimensions is intractable due to the maximization over high dimensional integrals thus severely restricting ist applicability so far. Czado (1996) developed a Markov Chain Monte Carlo (MCMC) algorithm to overcome this difficulty. In this paper we present an application of this algorithm to unemployment data from the Panel Study of Income Dynamics involving 11 waves of the panel study. In addition we adapt Bayesian model checking techniques based on the posterior predictive distribution (see for example Gelman et al. (1996)) for the multivariate probit model. These help to identify mean and correlation specification which fit the data well. C. Czado was supported by research grant OGP0089858 of the Natural Sciences and Engineering Research Council of Canada.     

Detecting homogeneous segments in DNA sequences by using hidden Markov models

In recent years there has been a rapid growth in the amount of DNA being sequenced and in its availability through genetic databases. Statistical techniques which identify structure within these sequences can be of considerable assistance to molecular biologists particularly when they incorporate the discrete nature of changes caused by evolutionary processes. This paper focuses on the detection of homogeneous segments within heterogeneous DNA sequences. In particular, we study an intron from the chimpanzee α-fetoprotein gene; this protein plays an important role in the embryonic development of mammals. We present a Bayesian solution to this segmentation problem using a hidden Markov model implemented by Markov chain Monte Carlo methods. We consider the important practical problem of specifying informative prior knowledge about sequences of this type. Two Gibbs sampling algorithms are contrasted and the sensitivity of the analysis to the prior specification is investigated. Model selection and possible ways to overcome the label switching problem are also addressed. Our analysis of intron 7 identifies three distinct homogeneous segment types, two of which occur in more than one region, and one of which is reversible.     

Binary probability maps using a hidden conditional autoregressive Gaussian process with an application to Finnish common toad data

The Finnish common toad data of Heikkinen and Hogmander are reanalysed using an alternative fully Bayesian model that does not require a pseudolikelihood approximation and an alternative prior distribution for the true presence or absence status of toads in each 10 km×10 km square. Markov chain Monte Carlo methods are used to obtain posterior probability estimates of the square-specific presences of the common toad and these are presented as a map. The results are different from those of Heikkinen and Hogmander and we offer an explanation in terms of the prior used for square-specific presence of the toads. We suggest that our approach is more faithful to the data and avoids unnecessary confounding of effects. We demonstrate how to extend our model efficiently with square-specific covariates and illustrate this by introducing deterministic spatial changes.     

如何认识和研究习近平关于教育的重要论述

习近平关于教育的重要论述蕴含教育的发展战略、理论主张和实践智慧,是深化教育改革的基本遵循和强大思想武器。学习研究习近平关于教育的重要论述是教育理论创新和传播的需要,也是思想解放和思想统一的需要。概括提炼性研究、比较性研究、阐释性研究是研究习近平关于教育的重要论述的三种基本研究类型。全局、文本、历史、时代、理论、实践六个维度为深入分析和把握习近平关于教育的重要论述提供了一个综合分析框架。     

新世纪学报工作的发展研究

形势的发展,要求学报进行创新式的工作,将学报的发展与市场经济建设相联系;由此,大推进式的建构学报工作的新路径。     

苏霍姆林斯基论校长素质及其现实价值

实施素质教育需要高素质的教育工作者,尤其需要高素质的校长。苏霍姆林斯基认为校长应具有正确的教育思想、坚定的优师理念、笃深的职业情感、渊博的文化知识、精湛的指导艺术和强烈的科研意识,这在实施素质教育的大背景下,对于强化校长素养,优化管理行为,具有特别重要的指导价值。     

以孔子体育教育思想为镜观中国教育思想中轻体的痼疾

孔子的教育思想影响中国两千年,但由于历史的错误,其光辉的体育教育思想一度被淹没,面对当今轻体思潮依然严重的现状,我们有必要还孔子体育教育思想以本来面目,并借以抨击现有轻体思潮的痼疾,以求得我们的体育教育能朝着健康的方向发展.     

论文化素质教育与人才创新素质的培养

大学文化素质教育是一种创新意识的教育,大学文化素质教育可以有效地促进人才的创新思维.文化素质教育与科技创新教育有着异曲同工之妙,二者都离不开想象力,在创作灵感上都有相似性,二者都具有渗透性.