Four tests of fit for the beta-binomial distribution

Tests based on the Anderson–Darling statistic, a third moment statistic and the classical Pearson–Fisher X ² statistic, along with its third-order component, are considered. A small critical value and power study are given. Some examples illustrate important applications.     

Unaliasing of aliased line component frequencies

This paper is concerned with undoing aliasing effects, which arise from discretely sampling a continuous‐time stochastic process. Such effects are manifested in the frequency‐domain relationships between the sampled and original processes. The authors describe a general technique to undo aliasing effects, given two processes, one being a time‐delayed version of the other. The technique is based on the observations that certain phase information between the two processes is unaffected by sampling, is completely determined by the (known) time delay, and contains sufficient information to undo aliasing effects. The authors illustrate their technique with a simulation example. The theoretical model is motivated by the helioseismological problem of determining modes of solar pressure waves. The authors apply their technique to solar radio data, and conclude that certain low‐frequency modes known in the helioseismology literature are likely the result of aliasing effects. The Canadian Journal of Statistics 38: 116–135; 2010 © 2010 Statistical Society of Canada     

Prediction based classification for longitudinal biomarkers

Assessment of circulating CD4 count change over time in HIV-infected subjects on antiretroviral therapy (ART) is a central component of disease monitoring. The increasing number of HIV-infected subjects starting therapy and the limited capacity to support CD4 count testing within resource-limited settings have fueled interest in identifying correlates of CD4 count change such as total lymphocyte count, among others. The application of modeling techniques will be essential to this endeavor due to the typically non-linear CD4 trajectory over time and the multiple input variables necessary for capturing CD4 variability. We propose a prediction based classification approach that involves first stage modeling and subsequent classification based on clinically meaningful thresholds. This approach draws on existing analytical methods described in the receiver operating characteristic curve literature while presenting an extension for handling a continuous outcome. Application of this method to an independent test sample results in greater than 98% positive predictive value for CD4 count change. The prediction algorithm is derived based on a cohort of n = 270 HIV-1 infected individuals from the Royal Free Hospital, London who were followed for up to three years from initiation of ART. A test sample comprised of n = 72 individuals from Philadelphia and followed for a similar length of time is used for validation. Results suggest that this approach may be a useful tool for prioritizing limited laboratory resources for CD4 testing after subjects start antiretroviral therapy.     

Estimating Individual-Level Risk in Spatial Epidemiology Using Spatially Aggregated Information on the Population at Risk

We propose a novel alternative to case-control sampling for the estimation of individual-level risk in spatial epidemiology. Our approach uses weighted estimating equations to estimate regression parameters in the intensity function of an inhomogeneous spatial point process, when information on risk-factors is available at the individual level for cases, but only at a spatially aggregated level for the population at risk. We develop data-driven methods to select the weights used in the estimating equations and show through simulation that the choice of weights can have a major impact on efficiency of estimation. We develop a formal test to detect non-Poisson behavior in the underlying point process and assess the performance of the test using simulations of Poisson and Poisson cluster point processes. We apply our methods to data on the spatial distribution of childhood meningococcal disease cases in Merseyside, U.K. between 1981 and 2007.     

A Class of Convolution‐Based Models for Spatio‐Temporal Processes with Non‐Separable Covariance Structure

Abstract. In this article, we propose a new parametric family of models for real‐valued spatio‐temporal stochastic processes S ( x , t ) and show how low‐rank approximations can be used to overcome the computational problems that arise in fitting the proposed class of models to large datasets. Separable covariance models, in which the spatio‐temporal covariance function of S ( x , t ) factorizes into a product of purely spatial and purely temporal functions, are often used as a convenient working assumption but are too inflexible to cover the range of covariance structures encountered in applications. We define positive and negative non‐separability and show that in our proposed family we can capture positive, zero and negative non‐separability by varying the value of a single parameter.     

Asymptotics for the Hirsch Index

Abstract. The last decade methods for quantifying the research output of individual researchers have become quite popular in academic policy making. The h‐index (or Hirsch index) constitutes an interesting combined bibliometric volume/impact indicator that has attracted a lot of attention recently. It is now a common indicator, available for instance on the Web of Science. In this article, we establish the asymptotic normality of the empirical h‐index. The rate of convergence is non‐standard: , where f is the density of the citation distribution and n is the number of publications of a researcher. In case that the citations follow a Pareto‐type respectively a Weibull‐type distribution as defined in extreme value theory, our general result specializes well to results that are useful for practical purposes such as the construction of confidence intervals and pairwise comparisons for the h‐index. A simulation study for the Pareto‐type case shows that the asymptotic theory works well for moderate sample sizes already.     

Influence diagnostics in a multivariate normal regression model with general parameterization

Patriota and Lemonte [24] introduced a quite general multivariate normal regression model. This model considers that the mean vector and the covariance matrix share the same vector of parameters. In this paper we present some influence assessment for this model, such as the local influence, total local influence of an individual and generalized leverage which are discussed. Additionally, the normal curvatures for local influence studies are derived under some perturbation schemes.     

Young Moroccan-Dutch: Thinking in Dutch,Feeling Moroccan

Abstract

We examined to what extent Moroccan-Dutch (N?=?25) between 15 and 32 years of age are oriented towards the Dutch and Moroccan community when describing their identities. We used the Twenty Statements Test (developed by Kuhn and McPartland (1954). See, M. H. Kuhn and T. S. McPartland, “An Empirical Investigation of Self-attitudes”, American Sociological Review, Vol. 19, No. 1, 1954, p. 69. In this study, participants were asked to complete the sentence with ‘I am..’ 20 times to assess how they described themselves.) and in-depth interviews to understand their challenges on the acculturation path since many face disadvantage and exclusion in the Dutch community. Moroccan-Dutch were, compared to the reference groups of mainstream Dutch (N?=?20) and Moroccans (N?=?25), more proud of their ethnic identity and attached to Islam. Though their Dutch language was dominant, they felt more Moroccan. They praised the Dutch openness, but suffered from negative stereotyping. Their participation in the Dutch community was low. To enhance their position, social professionals working with Moroccan-Dutch could draw on the rich ethnic and faith traditions of this group to encourage their deeper engagement with the Dutch society.     

Reexamining the ethnic hierarchy of locational attainment: Evidence from Los Angeles

Because of a lack of data, the locational attainment literature has not incorporated documentation status into models examining group differences in neighborhood quality. I fill this void by using the Los Angeles Family and Neighborhood Survey, which permits the identification of undocumented respondents, allowing a reexamination of the ethnic structure of locational attainment in this important immigrant-receiving city. Results first suggest that while undocumented Latinos live in the poorest quality communities, blacks live in neighborhoods that are similar to native-born Latinos and better than foreign-born Asians and Latinos. Second, the effects of education are strongest for blacks, allowing the highly educated an opportunity to reside in communities that are of better quality than educated Latinos and Asians. Thus, undocumented Latinos replace blacks at the bottom of the locational attainment hierarchy, allowing educated blacks in Los Angeles to reside in better neighborhoods than blacks in the nation at large.