Race–gender Differences in Risk and Protective Factors among Youth in Residential Group Homes

Analysis reported here examined youth in residential group home placements in order to better understand how sexual behaviors, drug use, and environmental experiences differ by race and gender subgroups. Data were collected from 336 youth aged 14–21 residing in one of 41 group homes in Maryland. Chi square and logistic regression analyses were done to determine differences on study variables among the race-gender groupings. Results demonstrate not only significant health concerns overall but notable differences were also found between race-gender subgroups. Findings suggest that found race-gender subgroup differences might be informative for tailoring programming for youth of residential group home settings.     

Optimal designs for discriminating between some extensions of the Michaelis–Menten model

In this paper some results on the computation of optimal designs for discriminating between nonlinear models are provided. In particular, some typical deviations of the Michaelis–Menten model are considered. A common deviation of this pharmacokinetic model consists on adding a linear term. If two linear models differ in one parameter the T-optimal design for discriminating between them is c-optimal for estimating the added linear term. This is not the case for nonlinear models.     

Estimation of total electricity consumption curves by sampling in a finite population when some trajectories are partially unobserved

Millions of smart meters that are able to collect individual load curves, that is, electricity consumption time series, of residential and business customers at fine scale time grids are now deployed by electricity companies all around the world. It may be complex and costly to transmit and exploit such a large quantity of information, therefore it can be relevant to use survey sampling techniques to estimate mean load curves of specific groups of customers. Data collection, like every mass process, may undergo technical problems at every point of the metering and collection chain resulting in missing values. We consider imputation approaches (linear interpolation, kernel smoothing, nearest neighbours, principal analysis by conditional estimation) that take advantage of the specificities of the data, that is to say the strong relation between the consumption at different instants of time. The performances of these techniques are compared on a real example of Irish electricity load curves under various scenarios of missing data. A general variance approximation of total estimators is also given which encompasses nearest neighbours, kernel smoothers imputation and linear imputation methods. The Canadian Journal of Statistics 47: 65–89; 2019 © 2018 Statistical Society of Canada     

RANKING ECONOMICS DEPARTMENTS IN EUROPE: A STATISTICAL APPROACH

We provide a ranking of economics departments in Europe and we discuss the methods used to obtain it. The JEL CD‐ROM serves as a database for a period covering ten years. Journals are ranked using a combination of expert opinions and citation data to produce a scale from 1 to 10. The publication output and habits of fifteen European countries plus California are then compared. Individuals with a contribution greater than a predetermined minimum level are regrouped into departments which are ranked according to their total scores. A standard deviation is provided to underline the uncertainty of this ranking. (JEL: I29, D63, C12, C14)     

Early Life Stress: Effects on the Regulation of Anxiety Expression in Children and Adolescents

This study examined children's (N = 79; 9–10 years) and adolescents’ (N = 82; 15–16 years) ability to regulate their emotion expressions of anxiety as they completed a modified version of the Trier Social Stress Test for Children (TSST‐C). Approximately half in each age group were internationally adopted from institutional care (N = 79) and half were non‐adopted, age‐matched peers (N = 82). Institutional care was viewed as a form of early life stress. Coders who were reliable and blind to group status watched videos of the session to assess anxiety expressions using the Child and Adolescent Stress and Emotion Scale developed for this study. Children exhibited more expressions of anxiety than adolescents, and youth adopted from institutions showed more expressions of anxiety than their non‐adopted counterparts. The role of early life stress on observed anxiety expressions remained significant after controlling for differences in age, physiological stress responses measured through salivary cortisol reactivity, and self‐reports of stress during the TSST‐C. This suggests possible deficits in the regulation of expressive behavior for youth with early life stress histories, which cannot be explained by experiencing the task as more stressful.     

Variance Estimation and Asymptotic Confidence Bands for the Mean Estimator of Sampled Functional Data with High Entropy Unequal Probability Sampling Designs

For fixed size sampling designs with high entropy, it is well known that the variance of the Horvitz–Thompson estimator can be approximated by the Hájek formula. The interest of this asymptotic variance approximation is that it only involves the first order inclusion probabilities of the statistical units. We extend this variance formula when the variable under study is functional, and we prove, under general conditions on the regularity of the individual trajectories and the sampling design, that we can get a uniformly convergent estimator of the variance function of the Horvitz–Thompson estimator of the mean function. Rates of convergence to the true variance function are given for the rejective sampling. We deduce, under conditions on the entropy of the sampling design, that it is possible to build confidence bands whose coverage is asymptotically the desired one via simulation of Gaussian processes with variance function given by the Hájek formula. Finally, the accuracy of the proposed variance estimator is evaluated on samples of electricity consumption data measured every half an hour over a period of 1 week.     

Réduction de la variance dans les sondages en présence d'information auxiliarie: Une approache non paramétrique par splines de régression

The author considers the use of auxiliary information available at population level to improve the estimation of finite population totals. She introduces a new type of model‐assisted estimator based on nonparametric regression splines. The estimator is a weighted linear combination of the study variable with weights calibrated to the B‐splines known population totals. The author shows that the estimator is asymptotically design‐unbiased and consistent under conditions which do not require the superpopulation model to be correct. She proposes a design‐based variance approximation and shows that the anticipated variance is asymptotically equivalent to the Godambe‐Joshi lower bound. She also shows through simulations that the estimator has good properties.     

Conceptualizing Puberty as a Window of Opportunity for Impacting Health and Well‐Being Across the Life Span

We promote the perspective that puberty, a separate biological process embedded in adolescence, is a “window of opportunity” for understanding and impacting health and development. First, we include a brief overview of pubertal change. Second, we propose a critical role for puberty in shaping life span health due to its connections with early life precursors, health issues and risks emerging during puberty, and health in young adulthood and beyond. Next, we discuss the importance of puberty measures in developmental research and suggest ways to make the science of puberty an important standalone research entity, as well as an essential component of studies conducted during adolescence. Finally, we discuss measurement issues, novel theoretical models of puberty, and the necessity of adopting an interdisciplinary perspective in research on puberty and in adolescence more broadly.     

Kernel density estimation on grouped data: the case of poverty assessment

Grouped data have been widely used to analyze the global income distribution because individual records from nationally representative household surveys are often unavailable. In this paper we evaluate the performance of nonparametric density smoothing techniques, in particular kernel density estimation, in estimating poverty from grouped data. Using Monte Carlo simulations, we show that kernel density estimation gives rise to nontrivial biases in estimated poverty levels that depend on the bandwidth, kernel, poverty indicator, size of the dataset, and data generating process. Furthermore, the empirical bias in the poverty headcount ratio critically depends on the poverty line. We also undertake a sensitivity analysis of global poverty estimates to changes in the bandwidth and show that they vary widely with it. A comparison of kernel density estimation with parametric estimation of the Lorenz curve, also applied to grouped data, suggests that the latter fares better and should be the preferred approach.