“罪名”抑或“具体犯罪行为”之析——对刑法第17条第2款有权解释的几点看法

相对刑事责任年龄的人犯罪,承担刑事责任能力与其年龄密切相关。立法上始终贯彻“以教育为主,惩罚为辅”的方针和“从宽处罚”的原则,对其承担刑事责任的范围作出了及其严格的限制规定。但如何确定犯罪的相对刑事责任年龄人承担刑事责任的范围、触犯的罪名等,在理论上还有待于进一步探讨。     

Measuring the health patterns of the ‘mixed/multiple’ ethnic group in Britain: data quality problems,reporting issues,and implications for policy

The ‘mixed’ group, officially recognised in the 2001 Census, is one of the most rapidly growing ethnic groups in Britain. Although ‘mixed’ categorisation was added to ethnic coding in NHS datasets, our knowledge of health patterns for this population is meagre. Data quality problems remain a key obstacle, including poor reproducibility of the data and constraints on reporting due to sparse data bias. The consequent minimal and indicative evidence base has focused mainly on risky health behaviours, mental health and generic measures of self-rated health, as it has in the U.S.A. and Canada. There is negligible information on the main underlying causes of death, such as neoplasms, heart disease and stroke. Consideration should be given to pooling data across multiple years of health and general purpose surveys to enable reporting for the four ‘mixed’ categories and adjustment for mediating factors and relevant confounders, such as measures of socio-economic status.     

Estimates of subgroup treatment effects in overall nonsignificant trials: To what extent should we believe in them?

In drug development, it sometimes occurs that a new drug does not demonstrate effectiveness for the full study population but appears to be beneficial in a relevant subgroup. In case the subgroup of interest was not part of a confirmatory testing strategy, the inflation of the overall type I error is substantial and therefore such a subgroup analysis finding can only be seen as exploratory at best. To support such exploratory findings, an appropriate replication of the subgroup finding should be undertaken in a new trial. We should, however, be reasonably confident in the observed treatment effect size to be able to use this estimate in a replication trial in the subpopulation of interest. We were therefore interested in evaluating the bias of the estimate of the subgroup treatment effect, after selection based on significance for the subgroup in an overall “failed” trial. Different scenarios, involving continuous as well as dichotomous outcomes, were investigated via simulation studies. It is shown that the bias associated with subgroup findings in overall nonsignificant clinical trials is on average large and varies substantially across plausible scenarios. This renders the subgroup treatment estimate from the original trial of limited value to design the replication trial. An empirical Bayesian shrinkage method is suggested to minimize this overestimation. The proposed estimator appears to offer either a good or a conservative correction to the observed subgroup treatment effect hence provides a more reliable subgroup treatment effect estimate for adequate planning of future studies.     

Your position in society matters for how happy you are

This article shows that people's perception of their position in society is strongly correlated with their level of happiness, and thus that differences in happiness levels among countries in different welfare state clusters are influenced by people's perceptions of their relative position in society (subjective position). The study drew on data from the European Social Survey. Two important findings emerged from the analysis. First, an individual's subjective position in society is a more important predictor of happiness than objective measures such as income, education and labour market position. Second, the link between individuals’ perceived position in society and their level of happiness is moderated by the welfare state. In the Nordic countries, people's perceptions of their position in society have less influence on happiness whereas in Eastern European countries we found a strong connection between subjective position and happiness.     

The role of social and cognitive factors in individual gambling: An empirical study on college students

Most of the studies on the determinants of individual gambling behaviour rely on cognitive theories. In our study, we argue that, besides cognitive factors, several social factors might play an important role as well. We analyse data from an ad hoc webmail survey conducted on about 2000 undergraduate students enrolled in a large public university in the Northern Italy in the academic year 2012–13. Using a variety of statistical techniques (standard regression models, boosted regression trees and structural equations models), we show that social variables affect both participation in gambling in the past year and latent gambling propensity. In particular, controlling for several proxies for individual cognitive ability and understanding of probability, gambling propensity is positively affected by the degree of gambling in the social surrounding (parents, peers, neighbourhood) and the acceptability of gambling activities to the individual. Moreover, in our sample of college students the role of social factors appears to be larger than that of cognitive factors, and this is consistent across different types of models and specifications.     

Inappropriate Comparisons: The LGB Population's Missing Reference Group

ABSTRACT

Given that many professional organizations emphasize the need for comparing assessment results to appropriate reference groups in their codes of ethics, we consider this practice through an ecological perspective as it applies to the lesbian, gay, and bisexual (LGB) population. The purpose of norming and types of assessment bias is discussed with respect to LGB population, which frequently lack specific norming data. The authors discuss whether the LGB population has characteristics similar to other commonly normed for groups and explore how group-shared constructs such as minority stress and internalized homophobia may introduce bias and affect the validity of assessments. A social anxiety assessment as well as a personality disorder inventory are examined for bias as case examples explaining how these assessments may exhibit bias when used with the LGB population. Increases in validity and effects on treatment planning are discussed as benefits of providing LGB-specific norms. Risks of providing these norms, such as minimizing subgroup differences and possibly increasing the risk of discrimination are also addressed before providing clinicians with recommendations as to how they may minimize the risk of bias in their assessment process.     

Monitoring the parameter vector of regression models with time-to-event response in phase II processes

In most of the existing specialized literature, monitoring regression models are a special case of profile monitoring. However, not every regression model always represents appropriately a profile data structure. This is clearly the case of the Weibull regression model (WRM) with common shape parameter γ. Even though it might be thought that existing methodologies (especially likelihood-ratio (LRT)-based methods) for monitoring generalized linear profiles can also be successfully applied to monitoring regression models with time-to-event response, it will be shown in this paper that those methodologies work fairly acceptable just for data structures with 1000 observations at least approximately. It was found out that some corrections, often referred to as Bartlett's adjustments, are needed to be implemented in order to improve the accuracy of using the asymptotic distributional properties of the LRT statistic for carrying out the monitoring of WRM with relatively small and moderate dimensions of the available datasets. Simulation studies suggest that the use of the aforementioned corrections make the resulting charts work quite acceptable when available data structures contain 30 observations at least. Detection abilities of the proposed schemes improve as dataset dimension increases.     

Merging data from multiple sources: pretest and shrinkage perspectives

In this article, we have developed asymptotic theory for the simultaneous estimation of the k means of arbitrary populations under the common mean hypothesis and further assuming that corresponding population variances are unknown and unequal. The unrestricted estimator, the Graybill-Deal-type restricted estimator, the preliminary test, and the Stein-type shrinkage estimators are suggested. A large sample test statistic is also proposed as a pretest for testing the common mean hypothesis. Under the sequence of local alternatives and squared error loss, we have compared the asymptotic properties of the estimators by means of asymptotic distributional quadratic bias and risk. Comprehensive Monte-Carlo simulation experiments were conducted to study the relative risk performance of the estimators with reference to the unrestricted estimator in finite samples. Two real-data examples are also furnished to illustrate the application of the suggested estimation strategies.     

Estimation of variance in bivariate normal distribution after the preliminary test of homogeneity

A preliminary test estimator of variance in the bivariate normal distribution is proposed after the Pitman–Morgan test of homogeneity of two variances. The bias and mean square error of the estimator are derived. The relative efficiency (RE) of the preliminary test estimator is studied. Computations and 3D graphs of RE for different parameters are analyzed. In order to get the maximum RE, recommendations of the significance level for the preliminary test are given for various sample sizes by using the max–min criterion.