Children's gender- and age-based categorization in similarity and induction tasks

The present studies examined children's and adults' preferences for gender- or age-based categorization using similarity and inductive inference tasks. Four-year-olds, 6-year-olds, and adults looked at pictures of people and decided which of two was more like a target (similarity condition) or which shared a novel age- or gender-related property with the target (inference condition). Age or gender-based matches were possible. The results are consistent with previous findings that gender-based classification decreases with age. However, they also demonstrate that children use gender more for judging similarity than for making inferences about novel properties. Distinct patterns emerge from the two tasks: 6-year-olds and adults in both conditions categorize more by age than gender; 4-year-olds categorize by gender more than age in the similarity task, but by age more in the induction task. Only adults differentiated by property in the inference condition. These findings suggest that the salience of gender categories cannot entirely be attributed to their inductive potential. Gender has a salience beyond what would be predicted by its power for directing novel generalizations.     

Naturally occurring play patterns in groups of latency aged children

This study examines the influence of the socialization of aggression among different gender and social class groupings and the influence of psychosocial stage on cognitive developmental play preferences among groups of school aged children. Observations of 143 play groups were coded for play preference (symbolic play, practice play, games with rules) and play performance (cooperative or competitive). Findings of the log-linear data analysis suggest a middle class and masculine bias in Piaget's cognitive model (i.e., that as age increases children prefer more games with rules). This model held only for middle class and affluent boys. Girls (both latency and prepubertal) and lower socioeconomic groups tended to prefer practice play. Girls were more likely to play cooperatively; boys more likely to play competitively. Implications for theory development and clinical practice are explored.     

Coping with work stress: Psychological benefits of exercise

Although the health-enhancing effects of physical exercise have been well documented, less is known about how exercise benefits stressed employees psychologically. This article examines exercise as a coping response, and identifies changes in self-schemata (a person's underlying belief systems about him or herself) as the means by which exercise affects coping resources. In addition, by drawing on schema theory and the exercise literature, the active components of an exercise programme and the individuals who might enhance their ability to cope with stress through such a programme are identified.     

Estimation of noncentrality parameters

We propose some estimators of noncentrality parameters which improve upon usual unbiased estimators under quadratic loss. The distributions we consider are the noncentral chi-square and the noncentral F. However, we give more general results for the family of elliptically contoured distributions and propose a robust dominating estimator.     

Statistics for weighing benefits and harms in a proposed genetic substudy of a randomized cancer prevention trial

Summary.  When evaluating potential interventions for cancer prevention, it is necessary to compare benefits and harms. With new study designs, new statistical approaches may be needed to facilitate this comparison. A case in point arose in a proposed genetic substudy of a randomized trial of tamoxifen versus placebo in asymptomatic women who were at high risk for breast cancer. Although the randomized trial showed that tamoxifen substantially reduced the risk of breast cancer, the harms from tamoxifen were serious and some were life threaten-ing. In hopes of finding a subset of women with inherited risk genes who derive greater bene-fits from tamoxifen, we proposed a nested case–control study to test some trial subjects for various genes and new statistical methods to extrapolate benefits and harms to the general population. An important design question is whether or not the study should target common low penetrance genes. Our calculations show that useful results are only likely with rare high penetrance genes.     

Proportional reversed hazard rate model and its applications

The purpose of this paper is to study the structure and properties of the proportional reversed hazard rate model (PRHRM) in contrast to the celebrated proportional hazard model (PHM). The monotonicity of the hazard rate and the reversed hazard rate of the model is investigated. Some criteria of aging are presented and the inheritance of the aging notions (of the base distribution) by the PRHRM is studied. Characterizations of the model involving Fisher information are presented and the statistical inference of the parameters is discussed. Finally, it is shown that several members of the proportional reversed hazard rate class have been found to be useful and flexible in real data analysis.     

HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.