Diagnostic tools in generalized Weibull linear regression models

We propose some statistical tools for diagnosing the class of generalized Weibull linear regression models [A.A. Prudente and G.M. Cordeiro, Generalized Weibull linear models, Comm. Statist. Theory Methods 39 (2010), pp. 3739–3755]. This class of models is an alternative means of analysing positive, continuous and skewed data and, due to its statistical properties, is very competitive with gamma regression models. First, we show that the Weibull model induces ma-ximum likelihood estimators asymptotically more efficient than the gamma model. Standardized residuals are defined, and their statistical properties are examined empirically. Some measures are derived based on the case-deletion model, including the generalized Cook's distance and measures for identifying influential observations on partial F-tests. The results of a simulation study conducted to assess behaviour of the global influence approach are also presented. Further, we perform a local influence analysis under the case-weights, response and explanatory variables perturbation schemes. The Weibull, gamma and other Weibull-type regression models are fitted into three data sets to illustrate the proposed diagnostic tools. Statistical analyses indicate that the Weibull model fitted into these data yields better fits than other common alternative models.     

Detection of outliers in simple circular regression models using the mean circular error statistic

The investigation on the identification of outliers in linear regression models can be extended to those for circular regression case. In this paper, we propose a new numerical statistic called mean circular error to identify possible outliers in circular regression models by using a row deletion approach. Through intensive simulation studies, the cut-off points of the statistic are obtained and its power of performance investigated. It is found that the performance improves as the concentration parameter of circular residuals becomes larger or the sample size becomes smaller. As an illustration, the statistic is applied to a wind direction data set.     

The Campus Alberta Applied Psychology Counselling Initiative: Web-Based Delivery of a Graduate Professional Training Program

SUMMARY

The Campus Alberta Applied Psychology: Counselling Initiative is a collaboration between three universities in western Canada to offer a predominantly Web-based Master of Counselling program. This paper describes the basic structure of the program, the Web-based delivery system, communication tools employed, and the philosophical, pedagogical, programmatic, and administrative principles and concepts foundation to the development and implementation. Development of curricular content and learning processes have been driven by explicit examination of learner needs, a program-level competency matrix reflective of professional practice standards in the discipline of psychology, and deliberate and continuous formative evaluation and learner engagement in the process.     

Shrinkage Structure of Partial Least Squares

Partial least squares regression (PLS) is one method to estimate parameters in a linear model when predictor variables are nearly collinear. One way to characterize PLS is in terms of the scaling (shrinkage or expansion) along each eigenvector of the predictor correlation matrix. This characterization is useful in providing a link between PLS and other shrinkage estimators, such as principal components regression (PCR) and ridge regression (RR), thus facilitating a direct comparison of PLS with these methods. This paper gives a detailed analysis of the shrinkage structure of PLS, and several new results are presented regarding the nature and extent of shrinkage.     

权力差距与国际贸易

权力差距,即霸权国与次等大国之间,霸权国与崛起的挑战国之间的差距,其大小驱动国际贸易水平的高低。该文试图说明,大国之间的权力差距是决定国际贸易走向的重要变量,将主要从两个方面入手:论述为什么权力差距是解释国际贸易波动的合理变量,以及其发生作用的因果机制;通过将1993-2008年世界贸易的状况与中美关系的发展情况进行比对,以此来对假说进行验证。     

Framing distance: local vs. non-local news in Hong Kong press

Geographical location as a “natural” attribute of news has always been a source of news manipulation. This research investigates how different community newspapers select, interpret, and package events originating in places of varying distances. Contextualized in Hong Kong, three media frames (authority, conflict, and attribution) are closely examined and theoretically connected to three types of newspapers (mass appeal, elite, and pro-establishment) across four locales (local, mainland China, Asian, and international). To test the hypotheses, a large-scale content analysis of 14 daily newspapers in Hong Kong was conducted. On the whole, findings supported the three hypotheses, showing that local news was systematically differentiated from non-local news in terms of the frequency of political authorities cited, presence of diversified opinions, and the likelihood of either individuals or institutions being blamed for social wrongs. Considerable variations were detected across different types of newspapers as well. Theoretical and social implications of the empirical findings are discussed.     

A multivariate solution of the multivariate ranking and selection problem

The problem of selection of the best multivariate population is given a new formulation which does not involve reducing the populations to univariate quantities. This formulation's solution is developed for known, and (using the Heteroscedastic Method) also for unknown, variance-covariance matrices. Preference reversals and arbitrary nonlinear preference functions are explicitly allowed in this new theory     

Estimation of signal to noise ratio using mahalanobis distance

In this paper, we propose some alternative estimatiors to that given by C. G. Khatri and C. R. Rao (1985), for estimating Signal to Noise ratio. Using Pitman Nearness, Condition for prefering one estimator over the other is estabilished. It is shown numerically that estimators corresponding to Entropy loss function are better more oftern than those corresponding to Squared Error loss.     

基于有效预测区域的模糊数据关联

多目标多传感器跟踪系统由数据关联和目标状态估计两部分组成,数据关联是多目标跟踪系统研究的核心。数据关联和目标状态估计两部分既有一定的独立性又有密切的联系,而将两部分合理地结合对提高跟踪系统的性能是重要的。该文以跟踪目标的有效预测区域为依据,利用基于Mahalanobis距离的模糊均值聚类方法解决数据关联问题,在一定程度上将数据关联和目标状态估计两个不同的过程相结合,仿真计算说明了其有效性。     

Interval censored and truncated data: Rate of convergence of NPMLE of the density

We consider survival data that are both interval censored and truncated. Under appropriate assumptions on the involved distributions, the censoring, truncation and survival, we prove the consistency of the NPMLE of the density of the survival, and give the rate of convergence. Finally, we give an example where the joint law of the censoring and truncation can be explicitly computed.