Assessing the Concept of the ‘Insecure‐Other’ Category in the Cassidy–Marvin Scheme: Changes Between 4 and 6 Years in the English and Romanian Adoptee Study

We set out to explore the meaning of the attachment categories in the Cassidy/Marvin strange situation procedure, as employed in the home, using data from a longitudinal study of children adopted into UK families up to the age of 42 months from Romanian institutions, and of adopted children without the experience of institutional care––both groups being assessed at 4 and 6 years of age. Inter‐rater reliability was found to be good. Security (meaning the use of the parent as a secure base and no negative behavior on reunion) was the modal categorical rating in both the institution‐reared and comparison groups, but the category of anomalous non‐normative behavior (meaning a lack of any ordered attachment behavior as covered by the standard ratings), previously labeled ‘insecure‐other’, was more common in the institution‐reared children. Because this was unassociated with the usual manifestations of insecurity (such as avoidance or resistance) and because it was largely evident in interactions with the stranger, it is concluded that the adjective ‘insecure’ was not appropriate. Although this ‘insecure‐other’ category was associated with disinhibited attachment as reported by parents (meaning a lack of differentiation among adults, a willingness to go off with strangers and a lack of checking back with parents in anxiety‐provoking situations), it did not prove to be a good index of disinhibited attachment because changes over time in the ‘insecure‐other’ category were not associated with changes in the rate of disinhibited attachment. Also, whereas the rate of ‘insecure‐other’ fell markedly in the institution‐reared group between 4 and 6 years of age, it rose in the comparison group, raising queries over its meaning.     

The Development of Peer Interaction in Infancy: Exploring the Dyadic Processes

Peer interaction is an important component of children's social repertoires that is associated with a variety of developmental outcomes and life skills. The present study provides an in‐depth study of early dyadic peer behaviors during the infancy period, during which social competence with peers is first being developed. Results from variable‐centered analyses highlight the effectiveness of behaviors, such as offering objects to peers, and point to the importance of the social context set by a peer's prior social behavior and processes for eliciting peer responses. Findings from person‐centered analyses reveal marked individual differences in the processes through which infants are successful in eliciting responses from their peers, illustrating the presence of multiple pathways to achieving social competence with peers.     

BAYESIAN SUBSET SELECTION AND MODEL AVERAGING USING A CENTRED AND DISPERSED PRIOR FOR THE ERROR VARIANCE

This article proposes a new data‐based prior distribution for the error variance in a Gaussian linear regression model, when the model is used for Bayesian variable selection and model averaging. For a given subset of variables in the model, this prior has a mode that is an unbiased estimator of the error variance but is suitably dispersed to make it uninformative relative to the marginal likelihood. The advantage of this empirical Bayes prior for the error variance is that it is centred and dispersed sensibly and avoids the arbitrary specification of hyperparameters. The performance of the new prior is compared to that of a prior proposed previously in the literature using several simulated examples and two loss functions. For each example our paper also reports results for the model that orthogonalizes the predictor variables before performing subset selection. A real example is also investigated. The empirical results suggest that for both the simulated and real data, the performance of the estimators based on the prior proposed in our article compares favourably with that of a prior used previously in the literature.     

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.     

Estimation of relaxation time distributions in magnetic resonance imaging

Magnetic resonance imaging techniques can be used to measure some biophysical properties of tissue. In this context, the T₂ relaxation time is an important parameter for soft‐tissue contrast. The authors develop a new technique to estimate the integral of the distribution of T₂ relaxation time without imposing any constraint other than the monotonicity of the underlying cumulative relaxation time distribution. They explore the properties of the estimation and its applications for the analysis of breast tissue data. As they show, an extension of linear discriminant analysis is found to distinguish well between two classes of breast tissue.     

Random regression models for human immunodeficiency virus ribonucleic acid data subject to left censoring and informative drop-outs

Objectives in many longitudinal studies of individuals infected with the human immunodeficiency virus (HIV) include the estimation of population average trajectories of HIV ribonucleic acid (RNA) over time and tests for differences in trajectory across subgroups. Special features that are often inherent in the underlying data include a tendency for some HIV RNA levels to be below an assay detection limit, and for individuals with high initial levels or high ranges of change to drop out of the study early because of illness or death. We develop a likelihood for the observed data that incorporates both of these features. Informative drop-outs are handled by means of an approach previously published by Schluchter. Using data from the HIV Epidemiology Research Study, we implement a maximum likelihood procedure to estimate initial HIV RNA levels and slopes within a population, compare these parameters across subgroups of HIV-infected women and illustrate the importance of appropriate treatment of left censoring and informative drop-outs. We also assess model assumptions and consider the prediction of random intercepts and slopes in this setting. The results suggest that marked bias in estimates of fixed effects, variance components and standard errors in the analysis of HIV RNA data might be avoided by the use of methods like those illustrated.     

Consistent Estimation of the Structural Distribution Function

Motivated by problems in linguistics we consider a multinomial random vector for which the number of cells N is not much smaller than the sum of the cell frequencies, i.e. the sample size n . The distribution function of the uniform distribution on the set of all cell probabilities multiplied by N is called the structural distribution function of the cell probabilities. Conditions are given that guarantee that the structural distribution function can be estimated consistently as n increases indefinitely although n / N does not. The natural estimator is inconsistent and we prove consistency of essentially two alternative estimators.     

Asymptotic distributions for the intervals estimators of the extremal index and the cluster-size probabilities

Under appropriate long range dependence conditions, the point process of exceedances of a stationary sequence weakly converges to a homogeneous compound Poisson point process. This limiting point process can be characterized by the extremal index and the cluster-size probabilities. In this paper we address the problem of estimating these quantities and we consider the intervals estimators introduced in Ferro and Segers [2003. Inference for clusters of extreme values. J. Roy. Statist. Soc. Ser. B 545–556] and in Ferro [2004. Statistical methods for clusters of extreme values. Ph.D. Thesis, Lancaster University]. We establish asymptotic weak convergence to Gaussian random variables and we give their asymptotic variance.     

Reports of Conferences,Institutes, and Seminars

This installment of "Serials Spoken Here" covers events that transpired between late September and late October 2008. Reported herein are two Webinars, one on ONIX for Serials, the other on SUSHI, and two conferences: the eighty-fourth annual Meeting of the Potomac Technical Processing Librarians and the New England Library Association's Annual Conference.