Construction of bivariate and multivariate weighted distributions via conditioning

Weighted distributions (univariate and bivariate) have received widespread attention over the last two decades because of their flexibility for analyzing skewed data. In this article, we propose an alternative method to construct a new family of bivariate and multivariate weighted distributions. For illustrative purposes, some examples of the proposed method are presented. Several structural properties of the bivariate weighted distributions including marginal distributions together with distributions of the minimum and maximum, evaluation of the reliability parameter, and verification of total positivity of order two are also presented. In addition, we provide some multivariate extensions of the proposed models. A real-life data set is used to show the applicability of these bivariate weighted distributions.     

Some alternative bivariate Kumaraswamy models

In this article we discuss various strategies for constructing bivariate Kumaraswamy distributions. As alternatives to the Nadarajah et al. (2011) bivariate model, four different models are introduced utilizing a conditional specification approach, a conditional survival function approach, and an Arnold–Ng bivariate beta distribution construction approach. Distributional properties for such bivariate distributions are investigated. Parameter estimation strategies for the models are discussed, as are the consequences of fitting two of the models to a particular data set involving the proportion of foggy days at two different airports in Colombia.     

Bivariate,multivariate, and matrix variate normal characterizations: A brief survey II

The paper entitled “Bivariate and Multivariate Normal Characterizations: A Brief Survey,” by Hamedani, which was published in 1992, covered the published characterizations of bivariate and multivariate normal (MVN) distributions from 1941 to 1991. The present work is a follow-up to the 1991/1992 survey which includes not only characterizations of the bivariate and MVN distributions, but also characterizations of the matrix variate normal distribution, which have appeared from 1991/1992 to the present.     

Community change for diverse democracy

This paper presents ideas about community change for diversedemocracy. It provides perspectives on change, identifies threetypes of practice, and examines some of its elements. It drawsupon secondary literature and empirical studies of practicein various communities and concludes with observations aboutfuture work in the field, with the general conclusion that democraticsocieties, especially ones whose populations are becoming morediverse, will depend in part on their ability to both recognizedifferences and also build bridges across cultural boundaries.The paper is based on the belief that strengthening the studyof these phenomena can contribute to their practice.     

Conditional moving linear regression: Modeling the recruitment process for ALLHAT

Effective recruitment is a prerequisite for successful execution of a clinical trial. ALLHAT, a large hypertension treatment trial (N = 42,418), provided an opportunity to evaluate adaptive modeling of recruitment processes using conditional moving linear regression. Our statistical modeling of recruitment, comparing Brownian and fractional Brownian motion, indicates that fractional Brownian motion combined with moving linear regression is better than classic Brownian motion in terms of higher conditional probability of achieving a global recruitment goal in 4-week ahead projections. Further research is needed to evaluate how recruitment modeling can assist clinical trialists in planning and executing clinical trials.     

畜牧业改变气候——澳洲打响同牲畜的减排战斗

有许多众所周知的方法可以减少二氧化碳的排放,例如:减少石化燃料的使用、使用太阳能或风力等再生能源、开省油的车子或替代能源车、使用省电的电子产品,并切实做到减量、重复使用和回收等。然而,当前有关全球变暖的辩论几乎都集中在能源及交通运输方面。     

‘The extreme end of a spectrum of violence’: physical abuse,hegemony and resistance in British residential care

While researching the history of traditional child care institutions – children's homes, orphanages, industrial schools and reformatories – the author was impressed by the similarities of regimen across the spectrum of traditional care. Underpinning all forms of care was a severe discipline which often became abusive. Sexual abuse was also reasonably common. There were differences but it is the similarities which are stressed in this article, which seeks reasons for the perceived sameness. Children in care came mostly from the same deprived social background, and no matter what the intentions of the carers, traditional care involved a confrontation with cherished working class values which many of the children were bound to resist. Resistance was met by severe staff reaction; hence the violent undercurrent. Copyright © 2001 John Wiley & Sons, Ltd.     

Alternative Methods for Computing the Sensitivity of Complex Surveillance Systems

Stochastic scenario trees are a new and popular method by which surveillance systems can be analyzed to demonstrate freedom from pests and disease. For multiple component systems—such as a combination of a serological survey and systematically collected observations—it can be difficult to represent the complete system in a tree because many branches are required to represent complex conditional relationships. Here we show that many of the branches of some scenario trees have identical outcomes and are therefore redundant. We demonstrate how to prune branches and derive compact representations of scenario trees using matrix algebra and Bayesian belief networks. The Bayesian network representation is particularly useful for calculation and exposition. It therefore provides a firm basis for arguing disease freedom in international forums.