Confidence Corridors for Multivariate Generalized Quantile Regression

We focus on the construction of confidence corridors for multivariate nonparametric generalized quantile regression functions. This construction is based on asymptotic results for the maximal deviation between a suitable nonparametric estimator and the true function of interest, which follow after a series of approximation steps including a Bahadur representation, a new strong approximation theorem, and exponential tail inequalities for Gaussian random fields. As a byproduct we also obtain multivariate confidence corridors for the regression function in the classical mean regression. To deal with the problem of slowly decreasing error in coverage probability of the asymptotic confidence corridors, which results in meager coverage for small sample sizes, a simple bootstrap procedure is designed based on the leading term of the Bahadur representation. The finite-sample properties of both procedures are investigated by means of a simulation study and it is demonstrated that the bootstrap procedure considerably outperforms the asymptotic bands in terms of coverage accuracy. Finally, the bootstrap confidence corridors are used to study the efficacy of the National Supported Work Demonstration, which is a randomized employment enhancement program launched in the 1970s. This article has supplementary materials online.     

Experiences,Burdens, and Support Needs in Siblings of Girls and Women with Anorexia Nervosa: Results from a Qualitative Interview Study

For many years, siblings of women and girls suffering from anorexia have been regarded as “the forgotten kin,” as they have been overlooked by both researchers and clinicians. Therefore, the goal of this explorative study was to investigate siblings’ experiences, burdens, and support needs in a differentiated manner. Siblings (n = 16) of patients with an ICD-10 diagnosis “anorexia nervosa” (F 50.0) took part in a qualitative, in-depth interview study; the data were assessed using content analysis. The results of this study should be especially interesting for clinical social work. Using the findings, a variety of concrete recommendations for psycho-social practice can be made, for example regarding the inclusion of siblings in treatment settings, as well as specific support concepts for siblings of anorexic women and girls in different age groups.     

Birth Outcome Measures and Infant Mortality

Recent studies have proposed alternative birth outcome measures as means of assessing infant mortality risk; nevertheless, there hasn’t yet been an integrated analysis of these approaches. We review 14 strategies, including various combinations of birth weight, gestational age, fetal growth rate, and Apgar scores—as predictors of early neonatal, late neonatal, and postneonatal mortality, and infant mortality. Using the NCHS linked birth/infant death file for 2001, we construct multivariate logit models and assess the associations between each of the 14 key birth outcome measures and four mortality outcomes. We find that all evaluated birth outcome measures are strong predictors, but Apgar scores are the strongest among all models for all outcomes, independent of birth weight and gestational age. Apgar scores’ predictive power is stronger for Mexican-, white-, and female-infants than for black- and male-infants. Second, all birth outcome measures remain significantly associated with mortality, but their predictive power reduces drastically over time. These findings suggest a rule of thumb for predicting infant mortality odds: when available, Apgar scores should always be included along with birth weight (or LBW status) and gestational age. Additionally, these findings argue for the continued study of low birthweight, gestational age, and Apgar scores as independently salient health outcomes.     

UK Wage Inequality: An Industry and Regional Perspective

Abstract. This paper looks at male wage inequality in the UK across industries and regions over a 15 year period. After controlling for the heterogeneity of productivity characteristics across the population, that part of wage inequality which cannot be explained by observable worker characteristics is examined. This is undertaken at both the industry and regional level to assess the key themes dominant in the literature capable of explaining within‐group wage inequality, namely: technology; globalization; female participation; immigration; shifts in the supply of relative education across cohorts; and falling unionization.     

Development of Vocal Protests From 3 to 18 Months

Infants often protest the activities of their caregivers, and this particular social interaction may provide an important window on early communication and its development. This study used naturalistic methods to investigate the development of vocal protests. Fifteen mother‐infant dyads at each of 5 ages, from 3 to 18 months, were observed at home. Maternal behaviors of caregiving and prohibiting were tallied from videotapes, as were infants' protests of these behaviors. Maternal caregiving decreased with age, but maternal prohibitions increased. There were no changes over age in the probability of protesting maternal caregiving behavior; however, 12‐month‐olds were more likely to protest prohibitions than 6‐ or 8‐month‐olds. Older infants were also more likely to use intense protests, such as screams, than younger infants. These age‐related changes were mirrored by the differences in prohibitions and protests observed between 8‐month‐olds who could crawl and those who could not. Findings from this study were related to previous research on infant crying as an important part of the prelinguistic communication system.     

A Polynomial Optimization Approach to Principal–Agent Problems

This paper presents a new method for the analysis of moral hazard principal–agent problems. The new approach avoids the stringent assumptions on the distribution of outcomes made by the classical first‐order approach and instead only requires the agent's expected utility to be a rational function of the action. This assumption allows for a reformulation of the agent's utility maximization problem as an equivalent system of equations and inequalities. This reformulation in turn transforms the principal's utility maximization problem into a nonlinear program. Under the additional assumptions that the principal's expected utility is a polynomial and the agent's expected utility is rational in the wage, the final nonlinear program can be solved to global optimality. The paper also shows how to first approximate expected utility functions that are not rational by polynomials, so that the polynomial optimization approach can be applied to compute an approximate solution to nonpolynomial problems. Finally, the paper demonstrates that the polynomial optimization approach extends to principal–agent models with multidimensional action sets.     

The efficacy of using judgmental versus quantitative forecasting methods in practice

In an era where forecasts drive entire supply chains forecasting is seen as an increasingly critical organizational capability. However, business forecasting continues to rely on judgmental methods despite large advancements in information technology and quantitative method capability, prompting calls for research to help understand the reasons behind this practice. Our study is designed to contribute to this knowledge by profiling differences between firms identified as primary users of either judgmental or quantitative forecasting methods. Relying on survey data from 240 firms we statistically analyzed differences between these categories of users based on a range of organizational and forecasting issues. Our study finds large differences in forecast error rates between the two groups, with users of quantitative methods significantly outperforming users of judgmental methods. The former are found to be equally prevalent regardless of industry, firm size, and product positioning strategy, documenting the benefits of quantitative method use in a variety of settings. By contrast, the latter are found to have significantly lower access to quantifiable data and to use information and technology to a lesser degree.