Suicide and mental health problems among Swedish youth in the wake of the 1990s recession

The purpose of this explorative study is to bring together a variety of Swedish data sources with indicators of youth mental health and living conditions in order to illuminate trends during the last decades, elucidate possible determinants of mental ill health and develop hypotheses to explain the observed trend patterns. The analyses in the study reveal some surprising inconsistencies with respect to the mental health trends among young people during the 1990s. Most striking is the pattern of increasing youth unemployment coinciding with almost inverse trends in the rates of suicide. The secular trends in fatal suicides during this period do not show any increase but some actual decrease in sub‐populations despite high sustained levels of unemployment. In contrast, survey data indicate that the general mental health of youth during this period appears to have deteriorated.     

Analysis of Competing Risks by Using Bayesian Smoothing

We consider the competing risks set-up. In many practical situations, the conditional probability of the cause of failure given the failure time is of direct interest. We propose to model the competing risks by the overall hazard rate and the conditional probabilities rather than the cause-specific hazards. We adopt a Bayesian smoothing approach for both quantities of interest. Illustrations are given at the end.     

On predictive distributions and Bayesian networks

In this paper we are interested in discrete prediction problems for a decision-theoretic setting, where the task is to compute the predictive distribution for a finite set of possible alternatives. This question is first addressed in a general Bayesian framework, where we consider a set of probability distributions defined by some parametric model class. Given a prior distribution on the model parameters and a set of sample data, one possible approach for determining a predictive distribution is to fix the parameters to the instantiation with the maximum a posteriori probability. A more accurate predictive distribution can be obtained by computing the evidence (marginal likelihood), i.e., the integral over all the individual parameter instantiations. As an alternative to these two approaches, we demonstrate how to use Rissanen's new definition of stochastic complexity for determining predictive distributions, and show how the evidence predictive distribution with Jeffrey's prior approaches the new stochastic complexity predictive distribution in the limit with increasing amount of sample data. To compare the alternative approaches in practice, each of the predictive distributions discussed is instantiated in the Bayesian network model family case. In particular, to determine Jeffrey's prior for this model family, we show how to compute the (expected) Fisher information matrix for a fixed but arbitrary Bayesian network structure. In the empirical part of the paper the predictive distributions are compared by using the simple tree-structured Naive Bayes model, which is used in the experiments for computational reasons. The experimentation with several public domain classification datasets suggest that the evidence approach produces the most accurate predictions in the log-score sense. The evidence-based methods are also quite robust in the sense that they predict surprisingly well even when only a small fraction of the full training set is used.     

The exponentiated power exponential regression model with different regression structures: application in nursing data

We define the exponentiated power exponential distribution and propose a regression model with different systematic structures based on the new distribution. We show that the new regression model can be applied to dispersion data since it represents a parametric family of models that includes as sub-models some widely-known regression models. It then can be used more effectively in the analysis of real data. We use maximum likelihood estimation and derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. Some global-influence measurements are also investigated and simulation studies are performed to evaluate the accuracy of the estimates. We provide an application of the regression model with four systematic structures to nursing activities score data in the Unit of the Medical Clinic of University of São Paulo (USP) Hospital.     

A semiparametric approach for modelling multivariate nonlinear time series

In this article, a semiparametric time‐varying nonlinear vector autoregressive (NVAR) model is proposed to model nonlinear vector time series data. We consider a combination of parametric and nonparametric estimation approaches to estimate the NVAR function for both independent and dependent errors. We use the multivariate Taylor series expansion of the link function up to the second order which has a parametric framework as a representation of the nonlinear vector regression function. After the unknown parameters are estimated by the maximum likelihood estimation procedure, the obtained NVAR function is adjusted by a nonparametric diagonal matrix, where the proposed adjusted matrix is estimated by the nonparametric kernel estimator. The asymptotic consistency properties of the proposed estimators are established. Simulation studies are conducted to evaluate the performance of the proposed semiparametric method. A real data example on short‐run interest rates and long‐run interest rates of United States Treasury securities is analyzed to demonstrate the application of the proposed approach. The Canadian Journal of Statistics 47: 668–687; 2019 © 2019 Statistical Society of Canada     

Confidence intervals based on the deviance statistic for the hyperparameters in state space models

The main objective of this work is to evaluate the performance of confidence intervals, built using the deviance statistic, for the hyperparameters of state space models. The first procedure is a marginal approximation to confidence regions, based on the likelihood test, and the second one is based on the signed root deviance profile. Those methods are computationally efficient and are not affected by problems such as intervals with limits outside the parameter space, which can be the case when the focus is on the variances of the errors. The procedures are compared to the usual approaches existing in the literature, which includes the method based on the asymptotic distribution of the maximum likelihood estimator, as well as bootstrap confidence intervals. The comparison is performed via a Monte Carlo study, in order to establish empirically the advantages and disadvantages of each method. The results show that the methods based on the deviance statistic possess a better coverage rate than the asymptotic and bootstrap procedures.     

Mobilities and peace

This article considers how an increasingly visible set of mobilities has implications for how peace and conflict are imagined and responded to. We are particularly interested in how these mobilities take form in everyday actions and shape new forms of peace and challenge existing ones. The article considers fixed categories associated with orthodox peace such as the international, borders and the state that are predicated on territorialism, centralized governance, and static citizenship. The article can be read as a critique of liberal peacebuilding and a contribution to current debates on migration, space and the everyday. Through conceptual scoping we develop the notion of mobile peace to characterize the fluid ways in which is being constructed through the mobilitiy of people and ideas.     

Intergenerational cooperation within the household: a Public Good game with three generations

We analyze cooperation of individuals in a family context, using a Public Good game. In a lab experiment, 165 individuals from 55 three-generation families (youth, parent, and grandparent) play a repeated Public Good game in three different treatments: one in which three members of the same family play each other (family), a second with the youth and two non-family members, while preserving the previous generational structure (inter-generational), and a third in which three randomly-selected players play each other (random). We find that all the age groups cooperate more when playing with relatives, indicating that family ties may have a positive relationship to contributions to the Public Good. We also find that this trend is more evident for the youths and the parents than for the grandparents. Furthermore, young individuals tend to cooperate less than older generations, especially in non-family treatments. Our results serve as evidence of the relationship between family ties and inter-generational cooperative behaviors.

     

The Importance of Racial Socialization: School‐Based Racial Discrimination and Racial Identity Among African American Adolescent Boys and Girls

This study examined various parental racial socialization messages as mediators between school‐based racial discrimination and racial identity formation over 4 years for African American boys (N = 639) and African American girls (N = 711). Findings indicated that school‐based racial discrimination was associated with racial identity beliefs. For African American boys, behavioral racial socialization messages mediated the relation between school‐based racial discrimination and racial centrality over time. Mediation also resulted for African American girls, but for a different set of race‐related messages (negative messages and racial barriers) and racial identity beliefs. The developmental significance of the findings and implications for future research are discussed.     

Interplay Between Sensation Seeking and Parental Rules in the Emergence of Heavy Episodic Drinking

This study examined the parallel mediational processes between sensation seeking and parental rules on alcohol, in the emergence of heavy episodic drinking (HED) in adolescents. Data were drawn from a U.K. clustered randomized control trial (control arm only, N ≈ 6,300, M_age at baseline = 12.5). Using parallel process latent growth curve analysis, stricter parental rules at baseline were found to be associated with greater declines in sensation seeking over time and a lower risk of HED at follow‐up (+33 months). Higher initial levels of sensation seeking predicted a faster relaxation of parental rules and a greater risk of HED. By maintaining strict rules about alcohol, parents may promote a positive reduction in sensation seeking and a lower risk of HED.