Minimum hellinger distance estimation for normal models

A robust estimator introduced by Beran (1977a, 1977b), which is based on the minimum Hellinger distance between a projection model density and a nonparametric sample density, is studied empirically. An extensive simulation provides an estimate of the small sample distribution and supplies empirical evidence of the estimator performance for a normal location-scale model. While the performance of the minimum Hellinger distance estimator is seen to be competitive with the maximum likelihood estimator at the true model, its robustness to deviations from normality is shown to be competitive in this setting with that obtained from the M-estimator and the Cramér-von Mises minimum distance estimator. Beran also introduced a goodness-of-fit statisticH ₂, based on the minimized Hellinger distance between a member of a parametric family of densities and a nonparametric density estimate. We investigate the statistic H (the square root of H ₂) as a test for normality when both location and scale are unspecified. Empirically derived critical values are given which do not require extensive tables. The power of the statistic H compares favorably with four other widely used tests for normality.     

Optimal collapsing of mixture distributions in robust recursive estimation

Several authors have discussed Kalman filtering procedures using a mixture of normals as a model for the distributions of the noise in the observation and/or the state space equations. Under this model, resulting posteriors involve a mixture of normal distributions, and a “collapsing method” must be found in order to keep the recursive procedure simple. We prove that the Kullback-Leibler distance between the mixture posterior and that of a single normal distribution is minimized when we choose the mean and variance of the single normal distribution to be the mean and variance of the mixture posterior. Hence, “collapsing by moments” is optimal in this sense. We then develop the resulting optimal algorithm for “Kalman filtering” for this situation, and illustrate its performance with an example.     

英汉远近指示语移情指示差异的文化解读

本文从心理距离理论入手,探讨英汉远近指示词“这/那(this/that)”移情指示差异,并探究其文化根源,旨在提高英语学习者的语用能力和跨文化交际能力.     

现代远程教育《秘书实务》课程学习支持模式研究

从学习支持服务的学习者、学习支持内容、学习支持策略等要素入手,在远程教育"教"与"学"时空分离的情况下,结合《秘书实务》课程重实践、重操作的课程特点,探索现代远程教育《秘书实务》课程学习支持模式的构建。     

INFLUENTIAL OBSERVATION IDENTIFICATION IN THE GROWTH CURVE MODEL WITH RAO'S SIMPLE COVARIANCE STRUCTURE

ABSTRACT

In this paper we discuss the identification of influential observations in a growth curve model with Rao's simple covariance structure. Based on the generalized Cook-type distance and the volume of a confidence ellipsoid, a variety of influence measures are proposed in terms of the case-deletion technique. Also, the influence of observations on a linear combination of regression coefficients is considered. For illustration, a practical example is analyzed using the proposed approach.     

Slowing Down Mobilities: Passengering on an Inter-war Ocean Liner

Abstract

This article contributes to recent work on the figure of the passenger and ‘passengering’ in mobilities research. It does so by considering the experiences of one passenger over two journeys on an ocean liner undertaken in 1930. The article ‘follows’ the passenger over the course of the two journeys in order to open up the processuality of passengering. The article makes two core contributions. First, it demonstrates the need to lend attention to the specific socialities and materialities of mobile modalities that work on and form the passenger. It is shown how specific assemblages come together at sea to create atmospheres that shape the experiences of long-distance travel over water. Second, by following the passenger over the course of journeying, it demonstrates the emergence of the passenger in relation to shifting affective atmospheres, not as a static figure but one of becoming passenger, as a shifting set of subjectivities, and, in turn, shows the value of ‘following’ a journey within mobilities research.     

The nearest proportional to size sampling design

The construction of a proportional to size sampling design nearest to a given design is introduced. The realization of the new design can be easily made with the help of the original one.     

An empirical goodness-of-fit test for multivariate distributions

An empirical test is presented as a tool for assessing whether a specified multivariate probability model is suitable to describe the underlying distribution of a set of observations. This test is based on the premise that, given any probability distribution, the Mahalanobis distances corresponding to data generated from that distribution will likewise follow a distinct distribution that can be estimated well by means of a large sample. We demonstrate the effectiveness of the test for detecting departures from several multivariate distributions. We then apply the test to a real multivariate data set to confirm that it is consistent with a multivariate beta model.     

Internationalizing Social Work Education Using Blackboard 6: INHOLLAND University,NL and James Madison University,USA

SUMMARY

Creating, delivering, and evaluating a pilot course in two countries and at two universities via Blackboard 6 presents pedagogical, cultural, and technological challenges as well as many rewards. The developmental process and negotiations required by both universities to bring the course to fruition are explored, including faculty workload, interactivity requirements connected to the course and credit assignment. The concepts of community building and cultural awareness are explored from faculty and students perspectives. Evaluation by the students and next steps from the developers' perspectives are important components supporting the efficacy of this approach.     

Classification with discrete and continuous variables via general mixed-data models

We study the problem of classifying an individual into one of several populations based on mixed nominal, continuous, and ordinal data. Specifically, we obtain a classification procedure as an extension to the so-called location linear discriminant function, by specifying a general mixed-data model for the joint distribution of the mixed discrete and continuous variables. We outline methods for estimating misclassification error rates. Results of simulations of the performance of proposed classification rules in various settings vis-à-vis a robust mixed-data discrimination method are reported as well. We give an example utilizing data on croup in children.