Young Adult and Parent Perceptions of Facilitation: Associations with Overparenting,Family Functioning,and Student Adjustment

Overparenting occurs when parents engage in developmentally inappropriate involvement in their children’s lives. This topic is salient in the context of young adults attending college, as this developmental stage necessitates independent decision-making and adjustment on the part of these students. At the same time, the turbulence involved in this transition might engender parental interference and facilitation that could impact young adults’ adjustment and perceived family functioning. This study used structural equation modeling to examine overparenting, perceived interference, facilitation, student adjustment, and family functioning in 302 parent–young adult dyads. Results indicated that overparenting was associated with facilitation, but not interference. Whereas this association was negative for young adults, it was positive for parents. This pattern was also reflected in the indirect effects of overparenting on student adjustment through young adults’ versus parents’ perceived facilitation. Young adults appear to have better outcomes when they perceive their parents to be facilitating their goals.     

A new algorithm for clustering based on kernel density estimation

In this paper, we present an algorithm for clustering based on univariate kernel density estimation, named ClusterKDE. It consists of an iterative procedure that in each step a new cluster is obtained by minimizing a smooth kernel function. Although in our applications we have used the univariate Gaussian kernel, any smooth kernel function can be used. The proposed algorithm has the advantage of not requiring a priori the number of cluster. Furthermore, the ClusterKDE algorithm is very simple, easy to implement, well-defined and stops in a finite number of steps, namely, it always converges independently of the initial point. We also illustrate our findings by numerical experiments which are obtained when our algorithm is implemented in the software Matlab and applied to practical applications. The results indicate that the ClusterKDE algorithm is competitive and fast when compared with the well-known Clusterdata and K-means algorithms, used by Matlab to clustering data.     

A robust automatic clustering algorithm for probability density functions with application to categorizing color images

This study develops a robust automatic algorithm for clustering probability density functions based on the previous research. Unlike other existing methods that often pre-determine the number of clusters, this method can self-organize data groups based on the original data structure. The proposed clustering method is also robust in regards to noise. Three examples of synthetic data and a real-world COREL dataset are utilized to illustrate the accurateness and effectiveness of the proposed approach.     

Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms

Competing risks occur in a time-to-event analysis in which a patient can experience one of several types of events. Traditional methods for handling competing risks data presuppose one censoring process, which is assumed to be independent. In a controlled clinical trial, censoring can occur for several reasons: some independent, others dependent. We propose an estimator of the cumulative incidence function in the presence of both independent and dependent censoring mechanisms. We rely on semi-parametric theory to derive an augmented inverse probability of censoring weighted (AIPCW) estimator. We demonstrate the efficiency gained when using the AIPCW estimator compared to a non-augmented estimator via simulations. We then apply our method to evaluate the safety and efficacy of three anti-HIV regimens in a randomized trial conducted by the AIDS Clinical Trial Group, ACTG A5095.     

Development of predictive signatures for treatment selection in precision medicine with survival outcomes

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker‐positive and biomarker‐negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood‐based change‐point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change‐point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.     

Demographic Comparison of American Individuals in Polyamorous and Monogamous Relationships

Research on polyamorous relationships has increased substantially over the past decade. This work has documented how polyamory is practiced and why individuals might pursue such arrangements. However, there is a lack of a systematic investigation of who is in polyamorous relationships and how they might differ from individuals in monogamous relationships. The present study is one of the first to address this by comparing the demographic backgrounds of individuals in polyamorous (N = 2,428) and monogamous (N = 539) relationships in the United States. Compared to participants in monogamous relationships, those in polyamorous relationships were more likely to report minority sexual identities. Despite similar age distributions, individuals in polyamorous relationships were more likely to report being in a civil union, being divorced, and earning less than $40,000 per year compared to individuals in monogamous relationships. People in polyamorous relationships were also more likely to select “other” options for most demographic characteristics, suggesting that they tend to choose less traditional response options in general. The current research highlights several demographic differences that need to be considered and potentially controlled for in future comparisons of polyamorous and monogamous relationships.     

Coercive controlling behaviors in intimate partner violence in male same-sex relationships:A mixed-methods study

Using mixed methods, we explored the role of coercive controlling behaviors in a high-risk sample of 126 men in violent same-sex relationships. Contrary to a prediction that separate factors of physical violence and coercive control might emerge, a simple principle components analysis supported that male same-sex relationship intimate partner violence (IPV) is essentially unidimensional. Qualitative narratives supported a single latent factor solution of violence, and that coercive controlling behaviors better detect IPV dynamics within the same violent encounters (i.e., weapon use), even when compared to profiles defined by physical violence. Narratives also highlighted gender-different tactics of coercion used, underscoring importance of context-based assessments.