Sex‐differentiated associations among negative parenting,emotion‐related brain function,and adolescent substance use and psychopathology symptoms

Parenting is a critical factor in adolescent social–emotional development, with maladaptive parenting leading to risk for the development of psychopathology. However, the emotion‐related brain mechanisms underlying the influence of parenting on psychopathology symptoms are unknown. The present study utilized functional magnetic resonance imaging and laboratory measures to examine sex‐differentiated associations among parenting, adolescent emotion‐related brain function, and substance use and psychopathology symptoms in 66 12–14‐year olds. Maternal parenting behaviors (warmth, negative parenting) were observed in a laboratory task. Adolescent brain responses to negative emotional stimuli were assessed in emotion processing regions of interest (left [L] and right [R] amygdala, anterior insula, and anterior cingulate cortex [ACC]). Adolescents reported on substance use and depressive, anxiety, and externalizing symptoms. Maternal negative parenting predicted adolescent brain activation differently by sex. For girls, negative parenting predicted heightened R ACC activation to negative emotional stimuli. For boys, negative parenting predicted blunted L and R anterior insula and L ACC activation. Furthermore, for girls, but not boys, heightened L anterior insula and heightened L and R ACC activation were associated with substance use and depressive symptoms, respectively. Findings suggest neural response to negative emotion as a possible sex‐specific pathway from negative parenting to psychopathology.     

A consistent estimator for logistic mixed effect models

We propose a consistent and locally efficient method of estimating the model parameters of a logistic mixed effect model with random slopes. Our approach relaxes two typical assumptions: the random effects being normally distributed, and the covariates and random effects being independent of each other. Adhering to these assumptions is particularly difficult in health studies where, in many cases, we have limited resources to design experiments and gather data in long‐term studies, while new findings from other fields might emerge, suggesting the violation of such assumptions. So it is crucial to have an estimator that is robust to such violations; then we could make better use of current data harvested using various valuable resources. Our method generalizes the framework presented in Garcia & Ma (2016) which also deals with a logistic mixed effect model but only considers a random intercept. A simulation study reveals that our proposed estimator remains consistent even when the independence and normality assumptions are violated. This contrasts favourably with the traditional maximum likelihood estimator which is likely to be inconsistent when there is dependence between the covariates and random effects. Application of this work to a study of Huntington's disease reveals that disease diagnosis can be enhanced using assessments of cognitive performance. The Canadian Journal of Statistics 47: 140–156; 2019 © 2019 Statistical Society of Canada     

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。     

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.     

Community sidelined: The loss of community focus in differential response

Differential response (DR) first emerged as one component of a child welfare paradigm that emphasized the need to engage communities in supporting families and children. However, the role of community in differential response has received little attention in recent literature. We examine the intellectual history of these ideas, tracing changes in the framing of community engagement in relation to DR over time. We find that attention to community has been sidelined by an increasingly narrow definition of DR that focuses on the existence of an alternative approach to engaging with screened in families, rather than the building of community support networks. There is currently no clear and explicit theoretical framework connecting community engagement to DR. We find that the absence of such a framework has given rise to a series of conceptual debates about the definition and purpose of DR. The development of a literature that elucidates the topic of community engagement in DR may serve to resolve some of these debates.