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.     

Locally quadratic log likelihood and data-based transformations

The investigation of multi-parameter likelihood functions is simplified if the log likelihood is quadratic near the maximum, as then normal approximations to the likelihood can be accurately used to obtain quantities such as likelihood regions. This paper proposes that data-based transformations of the parameters can be employed to make the log likelihood more quadratic, and illustrates the method with one of the simplest bivariate likelihoods, the normal two-parameter likelihood.     

A multiple model discrimination procedure

The case of selecting between a set of fixed models is considered. The true model is assumed to be contained in the set of proposed models and errors are taken to be normally distributed. A sequential procedure which yeilds probabilities of incorrect selections is proposed. The procedure is shown to have optimal properties and is extended to the estimated model case by a bootstrap procedure.     

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.     

Qdf misclassification probabilities for known population parameters

Approximated QDF misclassification probabilities have been derived for bivariate normal populations with known parameter values. Tne effect of unequal covariances and population distance on the misclassification probabilities are examined     

Ridge regression estimators in the linear regression models with non-spherical errors

The present paper considers a family of ordinary ridge regression estimators in the linear regression model when the disturbances covariance matrix depends upon a few unknown parameters. An asymptotic expansion for the distribution of the ridge regression estimator is developed and under the quadratic loss function its asymptotic risk is compared with that of the feasible GLS estimator.     

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.