论我国量刑程序模式的选择

量刑程序与定罪程序一样是刑事审判环节,具有独立性与依附性特征,而我国立法层面的量刑程序几乎是空白。我国在现有的司法环境下实行相对独立的量刑程序符合国际司法潮流,这一程序也没有增加各诉讼参加人的负担,较易操作落实,且有相关的配套程序和实践基础,因此具有可行性。但在实践中应注意一些问题,如要清晰控辩审三方在量刑程序中的定位,使社会调查报告机制走向制度化、专业化,判决书中应对量刑的依据和理由进行阐述等。     

Testing for linearity in generalized linear models using kernel smoothing

A test statistic proposed by Li (1999) for testing the adequacy of heteroscedastic nonlinear regression models using nonparametric kernel smoothers is applied to testing for linearity in generalized linear models. Simulation results for models with centered gamma and inverse Gaussian errors are presented to illustrate the performance of the resulting test compared with log-likelihood ratio tests for specific parametric alternatives. The test is applied to a data set of coronary heart disease status (Hosmer and Lemeshow, (1990).     

与国际行政法的产生有关的几个基本问题——基于全球化、全球治理的视角

全球治理的提出源于全球化的推动,而全球治理的提出及实践则使国际行政法的产生、形成成为可能。具体而言,全球公民社会的兴起与国际非政府组织的发展是国际行政法产生的两个现实驱动因素。国际行政法的产生路径可总结为"自下而上"和"自上而下"的路径。国际行政法具有区别于国内行政法的若干明显特征,国际层面相比国内层面的很多特殊性也使得国际行政法的体系化发展面临诸多挑战。这些问题虽然不能忽视,但它们也并非不可克服。虽然国际行政法不如国内行政法成熟,但是如能改进合法性,便能够将超国家机构的全球治理职能发挥得更好。全球治理语境下国际行政法体系的产生、形成是一种历史的必然趋势。     

当代女性择偶标准及其影响因素研究--基于365则女性征婚启事的内容分析

对2005年至2014年间刊登于五本杂志上的365则女性征婚启事进行内容分析和统计检验，发现在女性征婚者的自我陈述和择偶要求中，既有较为稳定的传统标准，也存在一些时代变化。女性自身的职业和文化程度提及率较高，择偶标准中道德品质和个性、个人素质等受到了更多关注。在社会资源方面，隐性的资源更被强调，择偶标准呈现多元化取向。在更年轻和文化程度更高的女性中，体现出了更具现代性的择偶标准。有婚史和有孩子的女性则更多地关注一些现实性的标准。     

Multinomial Selection in the Presence of Infinite Alternatives

We propose a new procedure for the multinomial selection problem to solve a real problem of any modern Air Force: the elaboration of better air-to-air tactics for Beyond Visual Range air-to-air combat that maximize its aircraft survival probability H(θ, ω), as well as enemy aircraft downing probability G(θ, ω). In this study, using a low-resolution simulator with generic parameters for the aircraft and missiles, we could increase an average success rate of 16.69% and 16.23% for H(θ, ω) and G(θ, ω), respectively, to an average success rate of 76.85% and 79.30%. We can assure with low probability of being wrong that the selected tactic has greater probability of yielding greater success rates in both H(θ, ω) and G(θ, ω) than any simulated tactic.     

A New Feature Selection Method for Text Categorization of Customer Reviews

With the rapid development of e-commerce, online consumer review plays an increasingly important role in consumers’ purchase decisions. Most research papers use the quantitative measures of consumer reviews for statistical analysis. Here we focus on analyzing the texts of customer reviews with text mining tools. We propose a new feature selection method called maximizing the difference. Various classification methods such as boosting, random forest and SVM are used to test the performance of the new method along with different evaluation criteria. Both simulation and empirical results show that it improves the effectiveness of the classifier over the existing methods.     

A Semiparametric Regression Model for Longitudinal Data with Non‐stationary Errors

Motivated by the need to analyze the National Longitudinal Surveys data, we propose a new semiparametric longitudinal mean‐covariance model in which the effects on dependent variable of some explanatory variables are linear and others are non‐linear, while the within‐subject correlations are modelled by a non‐stationary autoregressive error structure. We develop an estimation machinery based on least squares technique by approximating non‐parametric functions via B‐spline expansions and establish the asymptotic normality of parametric estimators as well as the rate of convergence for the non‐parametric estimators. We further advocate a new model selection strategy in the varying‐coefficient model framework, for distinguishing whether a component is significant and subsequently whether it is linear or non‐linear. Besides, the proposed method can also be employed for identifying the true order of lagged terms consistently. Monte Carlo studies are conducted to examine the finite sample performance of our approach, and an application of real data is also illustrated.     

Communicating Actionable Risk for Terrorism and Other Hazards⋆

We propose a shift in emphasis when communicating to people when the objective is to motivate household disaster preparedness actions. This shift is to emphasize the communication of preparedness actions (what to do about risk) rather than risk itself. We have called this perspective “communicating actionable risk,” and it is grounded in diffusion of innovations and communication theories. A representative sample of households in the nation was analyzed using a path analytic framework. Preparedness information variables (including content, density, and observation), preparedness mediating variables (knowledge, perceived effectiveness, and milling), and preparedness actions taken were modeled. Clear results emerged that provide a strong basis for communicating actionable risk, and for the conclusion both that information observed (seeing preparedness actions that other have taken) and information received (receiving recommendations about what preparedness actions to take) play key, although different, roles in motivating preparedness actions among the people in our nation.     

Bivariate versus univariate ordinal categorical data with reference to an ophthalmologic study

The Wisconsin Epidemiologic Study of Diabetic Retinopathy is a population-based epidemiological study carried out in Southern Wisconsin during the 1980s. The resulting data were analysed by different statisticians and ophthalmologists during the last two decades. Most of the analyses were carried out on the baseline data, although there were two follow-up studies on the same population. A Bayesian analysis of the first follow-up data, taken four years after the baseline study, was carried out by Angers and Biswas [Angers, J.-F. and Biswas, A., 2004, A Bayesian analysis of the four-year follow-up data of theWisconsin epidemiologic study of diabetic retinopathy. Statistics in Medicine, 23, 601–615.], where the choice of the best model in terms of the covariate inclusion is done, and estimates of the associated covariate effects were obtained using the baseline data to set the prior for the parameters. In the present article we consider an univariate transformation of the bivariate ordinal data, and a parallel analysis with the much simpler univariate data is carried out. The results are then compared with the results of Angers and Biswas (2004). In conclusion, our analyses suggest that the univariate analysis fails to detect features of the data found by the bivariate analysis. Even an univariate transformation of our data with quite high correlation with both left and right eyes is inadequate.     

A three-stage variable selection method for supersaturated designs

A supersaturated design (SSD) is a design whose run size is not enough for estimating all main effects. Such a design is commonly used in screening experiments to screen active effects based on the effect sparsity principle. Traditional approaches, such as the ordinary stepwise regression and the best subset variable selection, may not be appropriate in this situation. In this article, a new variable selection method is proposed based on the idea of staged dimensionality reduction. Simulations and several real data studies indicate that the newly proposed method is more effective than the existing data analysis methods.