Mean shift and influence measures in linear measurement error models with stochastic linear restrictions

We present influence diagnostics for linear measurement error models with stochastic linear restrictions using the corrected likelihood of Nakamura in 1990. The case deletion and mean shift outlier models are developed to identify outlying and influential observations. We derive a corrected score test statistic for outlier detection based on mean shift outlier models. The analogs of Cook's distance and likelihood distance are proposed to determine influential observations based on case deletion models. A parametric bootstrap procedure is used to obtain empirical distributions of the test statistics and a simulation study has been used to evaluate the performance of the proposed estimators based on the mean squares error criterion and the score test statistic. Finally, a numerical example is given to illustrate the theoretical results.     

Permutation tests of scale using deviances

Robust tests for comparing scale parameters, based on deviances—absolute deviations from the median—are examined. Higgins (2004) proposed a permutation test for comparing two treatments based on the ratio of deviances, but the performance of this procedure has not been investigated. A simulation study examines the performance of Higgins’ test relative to other tests of scale utilizing deviances that have been shown in the literature to have good properties. An extension of Higgins’ procedure to three or more treatments is proposed, and a second simulation study compares its performance to other omnibus tests for comparing scale. While no procedure emerged as a preferred choice in every scenario, Higgins’ tests are found to perform well overall with respect to Type I error rate and power.     

原产地多元化视角下的地方产业集群升级研究

地方产业集群升级是区域经济发展的重要研究主题,由于中国特别是东部地区产业集群具有"天生全球化"特质,基于价值链的集群升级理论在适用性上存在局限。本文以原产地多元化为切入点,依据产业集群嵌入全球价值链的程度,对其通过原产地多元化实现升级的途径进行探讨,并结合案例分析加以验证。进而提出基于原产地多元化实现地方产业集群升级的主要内容和政策建议,以期为现实的产业集群发展和区域经济政策制定提供启示。     

Testing variance components in balanced linear growth curve models

It is well known that the testing of zero variance components is a non-standard problem since the null hypothesis is on the boundary of the parameter space. The usual asymptotic chi-square distribution of the likelihood ratio and score statistics under the null does not necessarily hold because of this null hypothesis. To circumvent this difficulty in balanced linear growth curve models, we introduce an appropriate test statistic and suggest a permutation procedure to approximate its finite-sample distribution. The proposed test alleviates the necessity of any distributional assumptions for the random effects and errors and can easily be applied for testing multiple variance components. Our simulation studies show that the proposed test has Type I error rate close to the nominal level. The power of the proposed test is also compared with the likelihood ratio test in the simulations. An application on data from an orthodontic study is presented and discussed.     

Testing for linearity in generalized linear models using kernel smoothing

A test statistic proposed by Li (1999) for testing the adequacy of heteroscedastic nonlinear regression models using nonparametric kernel smoothers is applied to testing for linearity in generalized linear models. Simulation results for models with centered gamma and inverse Gaussian errors are presented to illustrate the performance of the resulting test compared with log-likelihood ratio tests for specific parametric alternatives. The test is applied to a data set of coronary heart disease status (Hosmer and Lemeshow, (1990).     

Two non parametric methods for change-point detection in distribution

The change-point detection problem is determining whether a change has taken place. Two non parametric methods based on empirical likelihood and the likelihood ratio are proposed for detecting a change-point problem in distributions for independent observations. Numerical studies are carried out to evaluate the performance of the proposed methods. The simulation results demonstrate that the proposed methods are robust, that is, they perform well regardless of whether the observations are from the same distribution family.     

与国际行政法的产生有关的几个基本问题——基于全球化、全球治理的视角

全球治理的提出源于全球化的推动,而全球治理的提出及实践则使国际行政法的产生、形成成为可能。具体而言,全球公民社会的兴起与国际非政府组织的发展是国际行政法产生的两个现实驱动因素。国际行政法的产生路径可总结为"自下而上"和"自上而下"的路径。国际行政法具有区别于国内行政法的若干明显特征,国际层面相比国内层面的很多特殊性也使得国际行政法的体系化发展面临诸多挑战。这些问题虽然不能忽视,但它们也并非不可克服。虽然国际行政法不如国内行政法成熟,但是如能改进合法性,便能够将超国家机构的全球治理职能发挥得更好。全球治理语境下国际行政法体系的产生、形成是一种历史的必然趋势。     

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.     

The Environmental Risks of Incomplete Globalisation

As the liberal optimism of the long 1990s has faded into a world of growing inequality and resurging nationalism, there is less certainty about the prospects of economic convergence and global integration. Beyond the formidable human cost of maintaining a divided world, the possibility of incomplete globalisation also gives rise to a number of environmental risks. While environmental political theory generally sees strength in localism, history rather shows that a robust world trade system is crucial to offset local resource scarcities and that cosmopolitan norms of solidarity are essential for helping communities to rebuild after environmental catastrophe. In relation to climate change, statist thinking has led to a focus on non-scalable technologies and a silent acceptance of chronic poverty abroad as a way of avoiding a climate emergency. Contrary to such views, this paper argues that accelerating the transition to a fully integrated high-energy planet may more effectively mitigate Anthropocene risks.     

Phase-II monitoring and diagnosing of multivariate categorical processes using generalized linear test-based control charts

In this paper, two control charts based on the generalized linear test (GLT) and contingency table are proposed for Phase-II monitoring of multivariate categorical processes. The performances of the proposed methods are compared with the exponentially weighted moving average-generalized likelihood ratio test (EWMA-GLRT) control chart proposed in the literature. The results show the better performance of the proposed control charts under moderate and large shifts. Moreover, a new scheme is proposed to identify the parameter responsible for an out-of-control signal. The performance of the proposed diagnosing procedure is evaluated through some simulation experiments.