Neighborhood history as a factor shaping syringe distribution networks among drug users at a U.S. syringe exchange

Throughout the U.S., high visibility drug markets are concentrated in neighborhoods with few economic opportunities, while drug buyers/users are widely dispersed. A study of Pittsburgh Syringe Exchange participants provides data on travel between and network linkages across neighborhoods with different levels of drug activity. There are distinct racial patterns to syringe distribution activity within networks and across neighborhoods. Pittsburgh's history suggests that these patterns emerge from historical patterns of social and economic development. Study data demonstrate the ability of IDUs to form long-term social ties across racial and geographic boundaries and use them to reduce the risk of HIV transmission.     

Racial, Ethnic, and Cultural Differences in Responding to Distinctiveness and Discrimination on Campus: Stigma and Common Group Identity

The present article examines how the salience of group membership can moderate or diffuse feelings of stigmatization for members of racial and ethnic minorities. A series of studies is presented that demonstrate that the development of a common group identity can diffuse the effects of stigmatization, improve intergroup attitudes, and enhance institutional satisfaction and commitment among college students and faculty. Strategies and interventions designed to address the effects of stigmatization, however, need to consider the different value and importance of racial and ethnic group identities for Whites and for people of color.     

Characterization of distributions by conditional expectation of generalized order statistics

In this work, general forms of many well-known continuous probability distributions are characterized by conditional expectation of some functions of generalized order statistics. These results are the generalization of the characterization results based on conditional expectation of the functions of order statistics given by Khan and Abu-Salih (1989).     

On a Bivariate Distribution with Exponential Marginals

A new bivariate distribution with exponential marginals has been introduced by Singpurwalla & Youngren (1993). This distribution is absolutely continuous and has a single parameter. It was originally motivated as the failure model for a two-component system experiencing damage described by a shot–noise process. The purpose of this paper is two-fold. The first is to articulate on several aspects of this distribution, in particular, its genesis, the nature of its dependence, its correlation structure, and its generalized version as a two-parameter bivariate distribution with exponential marginals. The second purpose of this paper is more general. Prompted by the need to explain certain features of the bivariate distribution, it is found useful to introduce a new notion in reliability and survival analysis. This notion is called the "hazard potential", of an item susceptible to failure. The hazard potential is viewed as a kind of hidden parameter of failure models that delineates a cause and effect relationship in reliability.     

A Comparison of Methods for Analysing a Nested Frailty Model to Child Survival in Malawi

Demographic and Health Surveys collect child survival times that are clustered at the family and community levels. It is assumed that each cluster has a specific, unobservable, random frailty that induces an association in the survival times within the cluster. The Cox proportional hazards model, with family and community random frailties acting multiplicatively on the hazard rate, is presented. The estimation of the fixed effect and the association parameters of the modified model is then examined using the Gibbs sampler and the expectation–maximization (EM) algorithm. The methods are compared using child survival data collected in the 1992 Demographic and Health Survey of Malawi. The two methods lead to very similar estimates of fixed effect parameters. However, the estimates of random effect variances from the EM algorithm are smaller than those of the Gibbs sampler. Both estimation methods reveal considerable family variation in the survival of children, and very little variability over the communities.     

An Automated MCEM Algorithm for Hierarchical Models with Multivariate and Multitype Response Variables

In this article, we consider a model allowing the analysis of multivariate data, which can contain data attributes of different types (e.g., continuous, discrete, binary). This model is a two-level hierarchical model which supports a wide range of correlation structures and can accommodate overdispersed data. Maximum likelihood estimation of the model parameters is achieved with an automated Monte Carlo expectation maximization algorithm. Our method is tested in a simulation study in the bivariate case and applied to a data set dealing with beehive activity.     

A Marginalized Combined Gamma Frailty and Normal Random-effects Model for Repeated,Overdispersed, Time-to-event Outcomes

This article proposes a marginalized model for repeated or otherwise hierarchical, overdispersed time-to-event outcomes, adapting the so-called combined model for time-to-event outcomes of Molenberghs et al. (in press Molenberghs, G., Verbeke, G., Efendi, A., Braekers, R., Demétrio, C. G.B. (in press). A combined gamma frailty and normal random-effects model for repeated, overdispersed time-to-event data. In press. [Google Scholar]), who combined gamma and normal random effects. The two sets of random effects are used to accommodate simultaneously correlation between repeated measures and overdispersion. The proposed version allows for a direct marginal interpretation of all model parameters. The outcomes are allowed to be censored. Two estimation methods are proposed: full likelihood and pairwise likelihood. The proposed model is applied to data from a so-called comet assay and to data from recurrent asthma attacks in children. Both estimation methods perform very well. From simulation results, it follows that the marginalized combined model behaves similarly to the ordinary combined model in terms of point estimation and precision. It is also observed that the pairwise likelihood required more computation time on the one hand but is less sensitive to starting values and stabler in terms of bias with increasing sample size and censoring percentage than full likelihood, on the other, leaving room for both in practice.