Moving racisms,shifting targets: comparing racism experienced by mothers of mixed-parentage children with racism experienced by young people seeking sanctuary in Britain

As revealed by the 2011 Census, England is increasingly multi-ethnic. Yet, at the same time, racist discourses and practices continue to remain salient. In order to explore the contemporary manifestations of racism, this paper draws on research with two groups occupying different spaces within the sociology of race and ethnicity: young separated migrants seeking sanctuary in Britain and lone white mothers of mixed-parentage children. In this paper, we examine the everyday and structural racism experienced by each group in order to consider how seemingly different manifestations of race and racism are linked together and, in certain ways, dependent on each other. The paper argues that the public celebration of mixedness acts as a disavowal mechanism which can be used to conceal the endurance of colour-based racism as well as state racism operating legally through the immigration system.     

Joint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: an application to acquired immune deficiency syndrome data

Summary.  In a large, prospective longitudinal study designed to monitor cardiac abnormalities in children born to women who are infected with the human immunodeficiency virus, instead of a single outcome variable, there are multiple binary outcomes (e.g. abnormal heart rate, abnormal blood pressure and abnormal heart wall thickness) considered as joint measures of heart function over time. In the presence of missing responses at some time points, longitudinal marginal models for these multiple outcomes can be estimated by using generalized estimating equations (GEEs), and consistent estimates can be obtained under the assumption of a missingness completely at random mechanism. When the missing data mechanism is missingness at random, i.e. the probability of missing a particular outcome at a time point depends on observed values of that outcome and the remaining outcomes at other time points, we propose joint estimation of the marginal models by using a single modified GEE based on an EM-type algorithm. The method proposed is motivated by the longitudinal study of cardiac abnormalities in children who were born to women infected with the human immunodeficiency virus, and analyses of these data are presented to illustrate the application of the method. Further, in an asymptotic study of bias, we show that, under a missingness at random mechanism in which missingness depends on all observed outcome variables, our joint estimation via the modified GEE produces almost unbiased estimates, provided that the correlation model has been correctly specified, whereas estimates from standard GEEs can lead to substantial bias.     

Multiechelon (R,S) Inventory Model*

Most studies in multiechelon inventory systems have concentrated on understanding the specific aspects of a system's behavior. The problem of optimal policy computation has largely been ignored. In this paper, we investigate a two-echelon inventory system experiencing stochastic demand and a pull system of inventory allocation. Both echelons use an order-up-to-level type control policy. A mathematical model is developed to determine the optimal order level at all echelons and validated through simulation. Two simple algorithms to locate the optimum solution are presented. The use of graphical tools in optimal policy calculation is also discussed.     

Prediction Error of Small Area Predictors Shrinking Both Means and Variances

The article considers a new approach for small area estimation based on a joint modelling of mean and variances. Model parameters are estimated via expectation–maximization algorithm. The conditional mean squared error is used to evaluate the prediction error. Analytical expressions are obtained for the conditional mean squared error and its estimator. Our approximations are second‐order correct, an unwritten standardization in the small area literature. Simulation studies indicate that the proposed method outperforms the existing methods in terms of prediction errors and their estimated values.     

Minimax weights for generalised M‐estimation in biased regression models

The authors consider the construction of weights for Generalised M‐estimation. Such weights, when combined with appropriate score functions, afford protection from biases arising through incorrectly specified response functions, as well as from natural variation. The authors obtain minimax fixed weights of the Mallows type under the assumption that the density of the independent variables is correctly specified, and they obtain adaptive weights when this assumption is relaxed. A simulation study indicates that one can expect appreciable gains in precision when the latter weights are used and the various sources of model uncertainty are present.     

Estimation of variance based on a ranked set sample

In this paper we examine the problem of the estimation of the variance σ² of a population based on a ranked set sample (RSS) from a nonparametric point of view. It is well known that based on a single cycle RSS, there does not exist an unbiased estimate of σ². We show that for more than one cycle, it is possible to construct a class of quadratic unbiased estimates of σ² in both balanced and unbalanced cases. Moreover, a minimum variance unbiased quadratic nonnegative estimate of σ² within a certain class of quadratic estimates is derived.     

Robust sequential designs for nonlinear regression

The authors introduce the formal notion of an approximately specified nonlinear regression model and investigate sequential design methodologies when the fitted model is possibly of an incorrect parametric form. They present small‐sample simulation studies which indicate that their new designs can be very successful, relative to some common competitors, in reducing mean squared error due to model misspecifi‐cation and to heteroscedastic variation. Their simulations also suggest that standard normal‐theory inference procedures remain approximately valid under the sequential sampling schemes. The methods are illustrated both by simulation and in an example using data from an experiment described in the chemical engineering literature.     

金砖国家合作机制与中印关系

进入21世纪以来,金砖国家经济发展迅猛,特别是金融危机后,金砖国家经济的强势崛起对世界经济政治产生了重要影响,随着金砖国家合作逐渐机制化,它必将对国际政治格局产生深远影响。本文分析了金砖国家机制的特点和西方国家对金砖国家合作机制的态度,在此基础上对印度支持金砖国家机制的缘由进行了阐述,最后分析了金砖国家机制对中印关系的促进作用。