Approach and Positive Affect in Toddlerhood Predict Early Childhood Behavior Problems

The aim of the study was to examine the moderating role of positive affect on the relation between approach behaviors and adjustment outcomes. One hundred eleven toddlers participated in a laboratory assessment of approach and positive affect at 24 months. Behavior problems were reported by a parent in the fall of the child's kindergarten year. Results supported our hypotheses that children who displayed high approach and high positive affect in both non‐threat and low‐threat contexts were rated as higher in externalizing behavior problems. On the other hand, for children showing low positive affect, increases in approach in a moderate‐threat context lowered the risk of developing internalizing behavior problems. Implications for these findings are discussed, including methodological considerations of differences among eliciting contexts and advantages of separating positive affect and approach.     

A Bayesian Model and Stochastic Exposure (Dose) Estimation for Relative Exposure Risk Comparison Involving Asbestos‐Containing Dropped Ceiling Panel Installation and Maintenance Tasks

Assessing exposures to hazards in order to characterize risk is at the core of occupational hygiene. Our study examined dropped ceiling systems commonly used in schools and commercial buildings and lay‐in ceiling panels that may have contained asbestos prior to the mid to late 1970s. However, most ceiling panels and tiles do not contain asbestos. Since asbestos risk relates to dose, we estimated the distribution of eight‐hour TWA concentrations and one‐year exposures (a one‐year dose equivalent) to asbestos fibers (asbestos f/cc‐years) for five groups of workers who may encounter dropped ceilings: specialists, generalists, maintenance workers, nonprofessional do‐it‐yourself (DIY) persons, and other tradespersons who are bystanders to ceiling work. Concentration data (asbestos f/cc) were obtained through two exposure assessment studies in the field and one chamber study. Bayesian and stochastic models were applied to estimate distributions of eight‐hour TWAs and annual exposures (dose). The eight‐hour TWAs for all work categories were below current and historic occupational exposure limits (OELs). Exposures to asbestos fibers from dropped ceiling work would be categorized as “highly controlled” for maintenance workers and “well controlled” for remaining work categories, according to the American Industrial Hygiene Association exposure control rating system. Annual exposures (dose) were found to be greatest for specialists, followed by maintenance workers, generalists, bystanders, and DIY. On a comparative basis, modeled dose and thus risk from dropped ceilings for all work categories were orders of magnitude lower than published exposures for other sources of banned friable asbestos‐containing building material commonly encountered in construction trades.     

A new approach to statistical efficiency of weighted least squares fitting algorithms for reparameterization of nonlinear regression models

We study nonlinear least-squares problem that can be transformed to linear problem by change of variables. We derive a general formula for the statistically optimal weights and prove that the resulting linear regression gives an optimal estimate (which satisfies an analogue of the Rao-Cramer lower bound) in the limit of small noise.     

Generalised Rank Regression Estimator with Standard Error Adjusted Lasso

One of the standard variable selection procedures in multiple linear regression is to use a penalisation technique in least‐squares (LS) analysis. In this setting, many different types of penalties have been introduced to achieve variable selection. It is well known that LS analysis is sensitive to outliers, and consequently outliers can present serious problems for the classical variable selection procedures. Since rank‐based procedures have desirable robustness properties compared to LS procedures, we propose a rank‐based adaptive lasso‐type penalised regression estimator and a corresponding variable selection procedure for linear regression models. The proposed estimator and variable selection procedure are robust against outliers in both response and predictor space. Furthermore, since rank regression can yield unstable estimators in the presence of multicollinearity, in order to provide inference that is robust against multicollinearity, we adjust the penalty term in the adaptive lasso function by incorporating the standard errors of the rank estimator. The theoretical properties of the proposed procedures are established and their performances are investigated by means of simulations. Finally, the estimator and variable selection procedure are applied to the Plasma Beta‐Carotene Level data set.     

Social Norms and Motivations Associated with College Binge Drinking*

This study proposes that members of Greek social organizations have higher rates of binge drinking as compared to other college students because of their unique social organization, which supports binge drinking. Using data from the College Alcohol Study, logistic regression analysis results show that Greek members binge drink at higher levels than do other students, which supports previous literature ( Cashin, Presley, and Meilman 1998 ). The results also indicate, however, that social norms and motives for drinking which were thought to be predictive of binge drinking practices for all students are actually better predictors of binge drinking for non‐Greek members.     

Observational Stance as a Predictor of Subjective and Genital Sexual Arousal in Men and Women

Observational stance refers to the perspective a person takes while viewing a sexual stimulus, either as a passive observer (observer stance) or an active participant (participant stance). The objective of the current study was to examine the relationship between observational stance and sexual arousal (subjective and genital) across a range of sexual stimuli that do or do not correspond with a participant's sexual attraction (preferred or nonpreferred stimuli, respectively). Regression analyses revealed that, for men (n = 44), participant stance significantly predicted subjective and genital arousal. Women's (n = 47) observer and participant stance predicted subjective arousal but not genital arousal. Analysis of variance showed that participant stance was greatest under preferred sexual stimuli conditions for all groups of participants, while observer stance scores revealed a less consistent pattern of response. This was particularly true for opposite-sex-attracted women, whose ratings of observer stance were lowest for preferred stimuli. Observational stance does not appear to account for gender differences in specificity of sexual arousal; for men, however, participant stance uniquely predicted genital response after controlling for sexual attractions. Similarities in the relationships between men's and women's observational stance and sexual responses challenge previous claims of gender differences in how men and women view erotica.     

Analysis of Regional Scale Risk of Whirling Disease in Populations of Colorado and Rio Grande Cutthroat Trout Using a Bayesian Belief Network Model

Introduction and spread of the parasite Myxobolus cerebralis, the causative agent of whirling disease, has contributed to the collapse of wild trout populations throughout the intermountain west. Of concern is the risk the disease may have on conservation and recovery of native cutthroat trout. We employed a Bayesian belief network to assess probability of whirling disease in Colorado River and Rio Grande cutthroat trout (Oncorhynchus clarkii pleuriticus and Oncorhynchus clarkii virginalis, respectively) within their current ranges in the southwest United States. Available habitat (as defined by gradient and elevation) for intermediate oligochaete worm host, Tubifex tubifex, exerted the greatest influence on the likelihood of infection, yet prevalence of stream barriers also affected the risk outcome. Management areas that had the highest likelihood of infected Colorado River cutthroat trout were in the eastern portion of their range, although the probability of infection was highest for populations in the southern, San Juan subbasin. Rio Grande cutthroat trout had a relatively low likelihood of infection, with populations in the southernmost Pecos management area predicted to be at greatest risk. The Bayesian risk assessment model predicted the likelihood of whirling disease infection from its principal transmission vector, fish movement, and suggested that barriers may be effective in reducing risk of exposure to native trout populations. Data gaps, especially with regard to location of spawning, highlighted the importance in developing monitoring plans that support future risk assessments and adaptive management for subspecies of cutthroat trout.     

Dose‐Response Models Incorporating Aerosol Size Dependency for Francisella tularensis

The effect of bioaerosol size was incorporated into predictive dose‐response models for the effects of inhaled aerosols of Francisella tularensis (the causative agent of tularemia) on rhesus monkeys and guinea pigs with bioaerosol diameters ranging between 1.0 and 24 μm. Aerosol‐size‐dependent models were formulated as modification of the exponential and β‐Poisson dose‐response models and model parameters were estimated using maximum likelihood methods and multiple data sets of quantal dose‐response data for which aerosol sizes of inhaled doses were known. Analysis of F. tularensis dose‐response data was best fit by an exponential dose‐response model with a power function including the particle diameter size substituting for the rate parameter k scaling the applied dose. There were differences in the pathogen's aerosol‐size‐dependence equation and models that better represent the observed dose‐response results than the estimate derived from applying the model developed by the International Commission on Radiological Protection (ICRP, 1994) that relies on differential regional lung deposition for human particle exposure.