Nonsuicidal Self‐Injury in a Community Sample of Adolescents: Subgroups,Stability, and Associations With Psychological Difficulties

Patterns of nonsuicidal self‐injury (NSSI) and their association with other psychological difficulties were studied in a community sample of young Swedish adolescents, by a two‐wave longitudinal design with a 1‐year interval. Hierarchical cluster analysis at T1 identified eight NSSI subgroups in each gender, of which five were clearly replicated at T2. Among these were a subgroup with generalized high‐frequency NSSI, which was associated with both internalizing and externalizing problems, and a subgroup of girls who engaged primarily in cutting and showed mainly internalizing problems. In general, NSSI clusters among girls were more stable over time and associated with more psychological problems. The results indicate that different patterns of NSSI have different developmental and clinical implications.     

Profiles of adult survivors of severe sexual,physical and emotional institutional abuse in Ireland

Adult survivors of institutional abuse were interviewed with a comprehensive assessment protocol which included the Childhood Trauma Questionnaire, the Institutional Child Abuse Processes and Coping Inventory, the Structured Clinical Interviews for Disorders of the Diagnostic and Statistical Manual of Mental Disorders IV axis I disorders and personality disorders, the Trauma Symptoms Inventory, a Life Problems Checklist, the Experiences in Close Relationships Inventory and the Kansas Marital Satisfaction Scale. Profiles were identified for subgroups that described severe sexual (N = 60), physical (N = 102), or emotional (N = 85) abuse as their worst forms of maltreatment. Survivors of severe sexual abuse had the most abnormal profile, which was characterised by higher rates of all forms of child maltreatment and higher rates of post‐traumatic stress disorder, alcohol and substance abuse, antisocial personality disorder, trauma symptoms and life problems. Survivors of severe emotional abuse were better adjusted than the other two groups. The profile of survivors of severe physical abuse occupied an intermediate position between the other two groups. A thorough assessment of abuse history and current functioning should be conducted when providing services to adult survivors of institutional abuse, since this may have important implications for the intensity of services required. Survivors of severe sexual abuse may require more intensive services. Copyright © 2009 John Wiley & Sons, Ltd.     

Logistic Regression Models with Unspecified Low Dose–Response Relationships and Experimental Designs for Hormesis Studies

Hormesis refers to a nonmonotonic (biphasic) dose–response relationship in toxicology, environmental science, and related fields. In the presence of hormesis, a low dose of a toxic agent may have a lower risk than the risk at the control dose, and the risk may increase at high doses. When the sample size is small due to practical, logistic, and ethical considerations, a parametric model may provide an efficient approach to hypothesis testing at the cost of adopting a strong assumption, which is not guaranteed to be true. In this article, we first consider alternative parameterizations based on the traditional three‐parameter logistic regression. The new parameterizations attempt to provide robustness to model misspecification by allowing an unspecified dose–response relationship between the control dose and the first nonzero experimental dose. We then consider experimental designs including the uniform design (the same sample size per dose group) and the $c$‐optimal design (minimizing the standard error of an estimator for a parameter of interest). Our simulation studies showed that (1) the $c$‐optimal design under the traditional three‐parameter logistic regression does not help reducing an inflated Type I error rate due to model misspecification, (2) it is helpful under the new parameterization with three parameters (Type I error rate is close to a fixed significance level), and (3) the new parameterization with four parameters and the $c$‐optimal design does not reduce statistical power much while preserving the Type I error rate at a fixed significance level.     

A Simple Adaptive Procedure Leading to Correlated Equilibrium

We propose a new and simple adaptive procedure for playing a game: ‘regret‐matching.’ In this procedure, players may depart from their current play with probabilities that are proportional to measures of regret for not having used other strategies in the past. It is shown that our adaptive procedure guarantees that, with probability one, the empirical distributions of play converge to the set of correlated equilibria of the game.     

Mechanism Design with Collusion and Correlation

In a public good environment with positively correlated types, we characterize optimal mechanisms when agents have private information and can enter collusive agreements. First, we prove a weak‐collusion‐proof principle according to which there is no restriction for the principal in offering weak‐collusion‐proof mechanisms. Second, with this principle, we characterize the set of allocations that satisfy individual and coalitional incentive constraints. The optimal weakly collusion‐proof mechanism calls for distortions away from first‐best efficiency obtained without collusion. Allowing collusion restores continuity between the correlated and the uncorrelated environments. When the correlation becomes almost perfect, first‐best efficiency is approached. Finally, the optimal collusion‐proof mechanism is strongly ratifiable.     

Labour Participation of Higher Education Students

The aim of this article is to shed light on the determinants of the decision to participate in the labour force while studying, and of the intensity of this participation as measured by the wages earned by students. We show that students react to their future expected economic benefits associated with their chosen course of study. In this sense, our results confirm Lévy‐Garboua’s (Revue française de Sociologie XVII: 53–80, 1976) thesis of working as an adjustment variable for the variations in the expected rate of return of discipline. Our results indicate that the decision to work while studying and its intensity depend on students’ socioeconomic status and material needs, as well as external financial resources. Altogether, our results suggest that the equity and internal efficiency implications of such a social bias in the labour force participation behaviour might not be too important, but that some public interventions might nevertheless improve the overall external efficiency by allowing students to spend more time on more valorizing activities rather than unskilled and low‐paid jobs.     

Central Limit Theorems for the Non‐Parametric Estimation of Time‐Changed Lévy Models

Abstract. Let {Z_t}_{t 0} be a Lévy process with Lévy measure ν and let be a random clock, where g is a non‐negative function and is an ergodic diffusion independent of Z. Time‐changed Lévy models of the form are known to incorporate several important stylized features of asset prices, such as leptokurtic distributions and volatility clustering. In this article, we prove central limit theorems for a type of estimators of the integral parameter β(?):=∫?(x)ν(dx), valid when both the sampling frequency and the observation time‐horizon of the process get larger. Our results combine the long‐run ergodic properties of the diffusion process with the short‐term ergodic properties of the Lévy process Z via central limit theorems for martingale differences. The performance of the estimators are illustrated numerically for Normal Inverse Gaussian process Z and a Cox–Ingersoll–Ross process .     

Second‐order bias‐corrected AIC in multivariate normal linear models under non‐normality

This paper deals with a bias correction of Akaike's information criterion (AIC) for selecting variables in multivariate normal linear regression models when the true distribution of observation is an unknown non‐normal distribution. It is well known that the bias of AIC is $O(1)$ , and there are a number of the first‐order bias‐corrected AICs which improve the bias to $O(n^{-1})$ , where $n$ is the sample size. A new information criterion is proposed by slightly adjusting the first‐order bias‐corrected AIC. Although the adjustment is achieved by merely using constant coefficients, the bias of the new criterion is reduced to $O(n^{-2})$ . Then, a variance of the new criterion is also improved. Through numerical experiments, we verify that our criterion is superior to others. The Canadian Journal of Statistics 39: 126–146; 2011 © 2011 Statistical Society of Canada