Child custody issues and co‐occurrence of intimate partner violence and child maltreatment: controversies and points of agreement amongst practitioners

The situation of families undergoing separation in a context of co‐occurrence of intimate partner violence (IPV) and child maltreatment raises certain issues related to child custody. The results presented in this paper were collected and analysed within the framework of a qualitative study aiming to identify the principal points of agreement and the main controversies amongst practitioners in several different types of organizations. Focus groups were held with a total of 43 practitioners from six different settings concerned with child custody in cases of co‐occurrence of IPV and child maltreatment. Although they agreed on the importance of ensuring the safety of victims of violence, their views diverged on three points: (1) the importance of preserving the father–child relationship; (2) collaboration between voluntary organizations and semi‐voluntary or legal agencies; and (3) consideration of cultural differences.     

It Takes Two to Fight in School,Too: A Social Relations Model of the Psychometric Properties and Relative Variance of Dyadic Aggression and Victimization in Middle School

Aggressive behavior has been well studied in terms of interindividual differences among aggressors and victims, but has been understudied, especially within naturalistic contexts, in terms of aggressor–victim relationships. The social relations model (SRM) is a powerful conceptual and analytic tool for studying dyadic phenomena, and we describe the use of multivariate SRM to study aggression. Boys and girls (N = 210) in middle schools (sixth and seventh grades) completed a newly created dyadic aggression and victimization inventory (DAVI). Results support the reliability and validity of the DAVI in assessing inter‐individual and inter‐dyadic differences in aggression and victimization. Occurrences of aggression were accounted for primarily by inter‐dyadic variability, indicating the importance of considering aggressor–victim relationships. We discuss the implications of this relationship focus for future research and intervention efforts.     

DENSITY ESTIMATION FOR CLUSTERED DATA

The commonly used survey technique of clustering introduces dependence into sample data. Such data is frequently used in economic analysis, though the dependence induced by the sample structure of the data is often ignored. In this paper, the effect of clustering on the non-parametric, kernel estimate of the density, f(x), is examined. The window width commonly used for density estimation for the case of i.i.d. data is shown to no longer be optimal. A new optimal bandwidth using a higher-order kernel is proposed and is shown to give a smaller integrated mean squared error than two window widths which are widely used for the case of i.i.d. data. Several illustrations from simulation are provided.     

A multiple test for comparing two treatments with control: Interval hypotheses approach

In biological experiments, multiple comparison test procedures may lead to a statistically significant difference in means. However, sometimes the difference is not worthy of attention considering the inherent variation in the characteristic. This may be due to the fact that the magnitude of the change in the characteristic under study after receiving the treatment is small, less than the natural biological variation. It then becomes the job of the statistician to design a test that will remove this paradox, such that the statistical significance will coincide with the biological one. The present paper develops a multiple comparison test for comparing two treatments with control by incorporating within-person variation in forming interval hypotheses. Assuming common variance (unknown) for the three groups (control and two treatments) and the width of the interval as intra-individual variation (known), the distribution of the test statistic is obtained as bivariate non-central t . A level f test procedure is designed. A table of critical values for carrying out the test is constructed for f = 0.05. The exact powers are computed for various values of small sample sizes and parameters. The test is powerful for all values of the parameters. The test was used to detect differences in zinc absorption for two cereal diets compared with a control diet. After application of our test, we arrived at the conclusion of homogeneity of diets with the control diet. Dunnett's procedure, when applied to the same data, concluded otherwise. The new test can also be applied to other data situations in biology, medicine and agriculture.     

Adaptive weights smoothing with applications to image restoration

We propose a new method of nonparametric estimation which is based on locally constant smoothing with an adaptive choice of weights for every pair of data points. Some theoretical properties of the procedure are investigated. Then we demonstrate the performance of the method on some simulated univariate and bivariate examples and compare it with other nonparametric methods. Finally we discuss applications of this procedure to magnetic resonance and satellite imaging.     

Designing and integrating composite networks for monitoring multivariate gaussian pollution fields

Networks of ambient monitoring stations are used to monitor environmental pollution fields such as those for acid rain and air pollution. Such stations provide regular measurements of pollutant concentrations. The networks are established for a variety of purposes at various times so often several stations measuring different subsets of pollutant concentrations can be found in compact geographical regions. The problem of statistically combining these disparate information sources into a single 'network' then arises. Capitalizing on the efficiencies so achieved can then lead to the secondary problem of extending this network. The subject of this paper is a set of 31 air pollution monitoring stations in southern Ontario. Each of these regularly measures a particular subset of ionic sulphate, sulphite, nitrite and ozone. However, this subset varies from station to station. For example only two stations measure all four. Some measure just one. We describe a Bayesian framework for integrating the measurements of these stations to yield a spatial predictive distribution for unmonitored sites and unmeasured concentrations at existing stations. Furthermore we show how this network can be extended by using an entropy maximization criterion. The methods assume that the multivariate response field being measured has a joint Gaussian distribution conditional on its mean and covariance function. A conjugate prior is used for these parameters, some of its hyperparameters being fitted empirically.     

Non-asymptotic sequential confidence regions with fixed sizes for the multivariate nonlinear parameters of regression

In this paper we consider a sequential design for the estimation of nonlinear parameters of regression with guaranteed accuracy. Non-asymptotic confidence regions with fixed sizes for the least squares estimates are used. The obtained confidence region is valid for finite numbers of data points when the distributions of the observations are unknown.     

A robust multivariate measurement error model with skew-normal/independent distributions and Bayesian MCMC implementation

Skew-normal/independent distributions are a class of asymmetric thick-tailed distributions that include the skew-normal distribution as a special case. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis in multivariate measurement errors models. We propose the use of skew-normal/independent distributions to model the unobserved value of the covariates (latent variable) and symmetric normal/independent distributions for the random errors term, providing an appealing robust alternative to the usual symmetric process in multivariate measurement errors models. Among the distributions that belong to this class of distributions, we examine univariate and multivariate versions of the skew-normal, skew-t, skew-slash and skew-contaminated normal distributions. The results and methods are applied to a real data set.