Estimation of spatial processes using local scoring rules

We display pseudo-likelihood as a special case of a general estimation technique based on proper scoring rules. Such a rule supplies an unbiased estimating equation for any statistical model, and this can be extended to allow for missing data. When the scoring rule has a simple local structure, as in many spatial models, the need to compute problematic normalising constants is avoided. We illustrate the approach through an analysis of data on disease in bell pepper plants.     

Effectiveness of combinations of Gaussian graphical models for model building

Combining statistical models is an useful approach in all the research area where a global picture of the problem needs to be constructed by binding together evidence from different sources [M.S. Massa and S.L. Lauritzen Combining Statistical Models, M. Viana and H. Wynn, eds., American Mathematical Society, Providence, RI, 2010, pp. 239–259]. In this paper, we investigate the effectiveness of combining a fixed number of Gaussian graphical models respecting some consistency assumptions in problems of model building. In particular, we use the meta-Markov combination of Gaussian graphical models as detailed in Massa and Lauritzen and compare model selection results obtained by combining selections over smaller sets of variables with selection results over all variables of interest. In order to do so, we carry out some simulation studies in which different criteria are considered for the selection procedures. We conclude that the combination performs, generally, better than global estimation, is computationally simpler by virtue of having fewer and simpler models to work on, and has an intuitive appeal to a wide variety of contexts.     

The Development of Dynamic Facial Expression Recognition at Different Intensities in 4- to 18-Year-Olds

The primary purpose of this study was to examine the effect of the intensity of emotion expression on children's developing ability to label emotion during a dynamic presentation of five facial expressions (anger, disgust, fear, happiness, and sadness). A computerized task (AFFECT—animated full facial expression comprehension test) was used to display facial emotion expressions as animations with four levels of intensity (35, 50, 75, and 100 percent). In this study, which employed a cross-sectional design, 240 participants from 4 to 18 years completed the AFFECT. Results indicated that recognition ability developed for each of the emotions, with the exception of disgust, over the age range tested. Girls were more accurate than boys, especially for anger and disgust expressions. Recognition accuracy was found to increase as a function of the intensity of emotional expressions.     

Who runs away from home and why? How families,schools, and bullying influence youth runaways

While prior research establishes that youth who experience family problems are more likely to leave home before reaching adulthood, we know less regarding how peer victimization in the form of bullying may influence youths' likelihood of becoming runaways. Youth often run away from home to escape family conflict or abusive home environments. Mental illness, behavioral problems, and delinquent peers are also powerful forces predicting the likelihood of running away from home. Additionally, recent literature suggests that negative experiences at school may increase youths' decisions to leave home prematurely. In this article, I review literature that addresses who runs away from home, including variations by age, race/ethnicity, sexual orientation, gender, family structure, mental health, problem behaviors, and peer networks. I also suggest directions for future research, including an approach that considers how families and schools individually and collectively affect youths' likelihood of running away from home. Finally, I recommend a focus on bully victimization to better understand how victimization outside of the family affects the likelihood that youth will become runaways.