Depression and Anxiety Symptoms in Female Adolescents: Relations with Parental Psychopathology and Parenting Behavior

Parental psychopathology and parenting behavior are known to be related to adolescents depression and anxiety, but unique roles of mothers and fathers are not clear. Our aim was to examine the relation of maternal and paternal psychopathology, emotional support, and respect for autonomy, and their interaction to depression and anxiety symptoms in adolescents. In total, 142 female adolescents participated, together with 138 mothers and 113 fathers. Data were analyzed using latent growth curve modeling. Paternal emotional support was negatively related to adolescent baseline level of depression and anxiety symptoms. Further, we found that there was a positive association between respect for autonomy and depression symptoms in adolescents for higher levels of paternal symptoms of psychological problems.     

Insight into Informant Discrepancies Regarding Psychosexual Functioning of Adolescents with and without Autism Spectrum Disorder

The private nature of psychosexual functioning leads adolescents and their parents to have different perspectives, which highlights studying parent–child informant discrepancies in this domain. We investigated informant discrepancy in psychosexual functioning, using the self‐report and parent report versions of the Teen Transition Inventory (TTI), of adolescents with autism spectrum disorder (ASD; 136 parent–child dyads) compared to adolescents from the general population (GP; 70 parent–child dyads). Significantly larger informant discrepancies exist in ASD dyads than GP dyads in most domains of psychosexual functioning, except for Body image, Sexual behavior, and Confidence in the future. It is important to use and pay attention to both informants, as discrepancies are relevant for both research and clinical practice regarding psychosexual functioning.     

Landscape Context Influences the Abundance and Richness of Native Lady Beetles Occupying Urban Vacant Land

Urban Ecosystems - Urban insect communities are structured by hierarchical filters including regional processes, human facilitation, and species interactions. We sought to inform future urban...     

Likelihood-free approximate Gibbs sampling

Likelihood-free methods such as approximate Bayesian computation (ABC) have extended the reach of statistical inference to problems with computationally intractable likelihoods. Such approaches perform well for small-to-moderate dimensional problems, but suffer a curse of dimensionality in the number of model parameters. We introduce a likelihood-free approximate Gibbs sampler that naturally circumvents the dimensionality issue by focusing on lower-dimensional conditional distributions. These distributions are estimated by flexible regression models either before the sampler is run, or adaptively during sampler implementation. As a result, and in comparison to Metropolis-Hastings-based approaches, we are able to fit substantially more challenging statistical models than would otherwise be possible. We demonstrate the sampler’s performance via two simulated examples, and a real analysis of Airbnb rental prices using a intractable high-dimensional multivariate nonlinear state-space model with a 36-dimensional latent state observed on 365 time points, which presents a real challenge to standard ABC techniques.     

Multiple event times in the presence of informative censoring: modeling and analysis by copulas

Motivated by a breast cancer research program, this paper is concerned with the joint survivor function of multiple event times when their observations are subject to informative censoring caused by a terminating event. We formulate the correlation of the multiple event times together with the time to the terminating event by an Archimedean copula to account for the informative censoring. Adapting the widely used two-stage procedure under a copula model, we propose an easy-to-implement pseudo-likelihood based procedure for estimating the model parameters. The approach yields a new estimator for the marginal distribution of a single event time with semicompeting-risks data. We conduct both asymptotics and simulation studies to examine the proposed approach in consistency, efficiency, and robustness. Data from the breast cancer program are employed to illustrate this research.