A goodness of fit test for multilevel survival data

No satisfactory goodness of fit test is available for multilevel survival data which occur when survival data are clustered or hierarchical in nature. Hence the aim of this research is to develop a new goodness of fit test for multilevel survival data and to examine the properties of the newly developed test. Simulation studies were carried out to evaluate the type ? error and the power. The results showed that the type I error holds for every combination tested and that the test is powerful against the alternative hypothesis of nonproportional hazards for all combinations tested.     

A revisit to test the equality of variances of several populations

We revisit the problem of testing homoscedasticity (or, equality of variances) of several normal populations which has applications in many statistical analyses, including design of experiments. The standard text books and widely used statistical packages propose a few popular tests including Bartlett's test, Levene's test and a few adjustments of the latter. Apparently, the popularity of these tests have been based on limited simulation study carried out a few decades ago. The traditional tests, including the classical likelihood ratio test (LRT), are asymptotic in nature, and hence do not perform well for small sample sizes. In this paper we propose a simple parametric bootstrap (PB) modification of the LRT, and compare it against the other popular tests as well as their PB versions in terms of size and power. Our comprehensive simulation study bursts some popularly held myths about the commonly used tests and sheds some new light on this important problem. Though most popular statistical software/packages suggest using Bartlette's test, Levene's test, or modified Levene's test among a few others, our extensive simulation study, carried out under both the normal model as well as several non-normal models clearly shows that a PB version of the modified Levene's test (which does not use the F-distribution cut-off point as its critical value), and Loh's exact test are the “best” performers in terms of overall size as well as power.     

POWER FUNCTION FOR INVERSE GAUSSIAN REGRESSION MODELS

Inverse Gaussian regression models are useful for data where both the independent and dependent variable are nonnegative and the variance of the dependent variable depends on the independent variable. Zero intercept inverse Gaussian regression models are presented with nonconstant variance, constant ratio of variance to the mean and constant coefficient of variation. The power function for testing hypotheses about the slope is given for all of these models.     

Power and dependence perspectives on outsourcing decisions

Since the 1980s outsourcing has been a major topic in both economic and management literature. Economic literature tends to focus on the transaction cost perspective. In management literature the core competence approach prevails. An emerging alternative view on outsourcing is the power and dependence perspective. Aspects of power and dependence become visible in four case studies involving the maintenance of capital assets. In each instance both the buyer and supplier sides of the dyad are examined. The paper concludes with managerial implications and research opportunities.     

论群体事件中行政紧急权力的运用与制约

伴随着改革开放的深入和经济发展取得举世瞩目的成就,我国体制与社会转型所带来的不同利益群体的矛盾和冲突,使我国进入了社会矛盾的高发期。群体事件的发生,呼吁着作为法自由和法权利支柱的权力介入。我国目前还存在机制转换不到位、忽视媒体执政权力使用、缺乏正确的权力观、机构设置过于专业和分散、紧急权力行使人员规则意识淡薄等问题。因此,当务之急是完善群体性事件中紧急权力的运用程序,提高媒体执政力,分离决策权和行使权,并做好协调和制约工作。     

身体、权力与教育：哈萨克族古代传统女性的养成

女性教育尤其是少数族群的女性教育是近些年学界讨论的热点问题之一。女性教育研究在关注当前之外，还应将视野扩宽至过去，以求对女性现状与发展有更为清楚的理解。文章以哈萨克族为例，解读哈萨克古代传统女性教育形成的深层原因。通过研究得知：在哈萨克族古代传统社会，女性的培养直接受制于其身体资本的制约，并受由身体资本弱势所致的权力资本丧失的限制。     

论批评性话语分析

本文探索了批评性话语分析的理论渊源,并论述了批评性话语分析的研究方法和研究范围,最后论文阐述了批评性话语分析的研究前景和研究意义,作者认为批评性话语分析在语言学界日益成为一个学术焦点,对批评性话语分析的研究能使公众认清话语中隐藏的权力、意识形态的真实面貌,同时在揭露权力话语对公众的欺诈性上是具有深远意义的。批评性话语分析的主要任务是提高公众对大众语篇的敏感性,培养公众的话语批判意识。     

Evaluating the Use of Spatial Covariance Structures in the Analysis of Cardiovascular Imaging Data

This article investigates the choice of working covariance structures in the analysis of spatially correlated observations motivated by cardiac imaging data. Through Monte Carlo simulations, we found that the choice of covariance structure affects the efficiency of the estimator and power of the test. Choosing the popular unstructured working covariance structure results in an over-inflated Type I error possibly due to a sample size not large enough relative to the number of parameters being estimated. With regard to model fit indices, Bayesian Information Criterion outperforms Akaike Information Criterion in choosing the correct covariance structure used to generate data.     

Exponentiality Test Based on the Progressive Type II Censoring via Cumulative Entropy

In this article, we use cumulative residual Kullback-Leibler information (CRKL) and cumulative Kullback-Leibler information (CKL) to construct two goodness-of-fit test statistics for testing exponentiality with progressively Type-II censored data. The power of the proposed tests are compared with the power of goodness-of-fit test for exponentiality introduced by Balakrishnan et al. (2007 Balakrishnan, N., Habibi Rad, A., Arghami, N.R. (2007). Testing exponentiality based on Kullback-Leibler information with progressively type-II censored data. IEEE Transactions on Reliability 56(2):301–307.[Crossref], [Web of Science ®] , [Google Scholar]). We show that when the hazard function of the alternative is monotone decreasing, the test based on CRKL has higher power and when the hazard function of the alternative is non-monotone, the test based on CKL has higher power. But, when it is monotone increasing the power difference between test based on CKL and their proposed test is not so remarkable. The use of the proposed tests is shown in an illustrative example.     

话语结构中的权势隐含

本文基于电视心理咨询的真实语料,用语言学谈话分析的方法,考察机构角色、话语角色,话语结构中的话轮转换,相邻语对等特征与之内含的话语权势的关系。通过微观层面的分析,本文指出"心理访谈"有别于一般的日常会话,它是一种具有非对等权势关系的谈话活动。话语权势不仅可以从上至下地实施,且可以通过特定的话语构架隐含其中。这种话语权势通常不被人们察觉,但它卻驾驭著人们的言语行为模式。