Conditional Akaike Information Criteria for a Class of Poisson Mixture Models with Random Effects

Focusing on the model selection problems in the family of Poisson mixture models (including the Poisson mixture regression model with random effects and zero‐inflated Poisson regression model with random effects), the current paper derives two conditional Akaike information criteria. The criteria are the unbiased estimators of the conditional Akaike information based on the conditional log‐likelihood and the conditional Akaike information based on the joint log‐likelihood, respectively. The derivation is free from the specific parametric assumptions about the conditional mean of the true data‐generating model and applies to different types of estimation methods. Additionally, the derivation is not based on the asymptotic argument. Simulations show that the proposed criteria have promising estimation accuracy. In addition, it is found that the criterion based on the conditional log‐likelihood demonstrates good model selection performance under different scenarios. Two sets of real data are used to illustrate the proposed method.     

Sparse group lasso for multiclass functional logistic regression models

Sparsity-inducing penalties are useful tools for variable selection and are also effective for regression problems where the data are functions. We consider the problem of selecting not only variables but also decision boundaries in multiclass logistic regression models for functional data, using sparse regularization. The parameters of the functional logistic regression model are estimated in the framework of the penalized likelihood method with the sparse group lasso-type penalty, and then tuning parameters for the model are selected using the model selection criterion. The effectiveness of the proposed method is investigated through simulation studies and the analysis of a gene expression data set.     

An m-estimation-based model selection criterion with a data-oriented penalty

In Wu and Zen (1999), a linear model selection procedure based on M-estimation is proposed, which includes many classical model selection criteria as its special cases, and it is shown that the selection procedure is strongly consistent for a variety of penalty functions. In this paper, we will investigate its small sample performances for some choices of fixed penalty functions. It can be seen that the performance varies with the choice of the penalty. Hence, a randomized penalty based on observed data is proposed, which preserves the consistency property and provides improved performance over a fixed choice of penalty functions.     

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。     

Applications of quadrivariate exponential distribution to a three-unit warm standby system with dependent structure

Two-unit warm standby systems have been elaborately dealt within the literature. However, the study of standby systems with more than two units, though very relevant in state-of-the-art practical situations, has received little attention because of mathematical intricacies involved in analyzing them. Also, such systems have been studied assuming: (i) the lifetime or repair time of the units to be exponential, or (ii) the lifetime and repair time to be independent. The present contribution is an improvement in the state-of-the-art in the sense that three-unit warm standby system with dependent structure is shown to be capable of comprehensive analysis.     

基于学分绩点与综合素质测评的换算模型建立

学生综合素质测评体系的完善对有效实施大学生综合素质测评工作以及指导大学生健康发展有重要意义,其中,学生综合素质测评体系的关键在于学分绩点与综合素质测评的换算模型的转化。通过分析高校现行学分绩点测评公式,发现其存在的主要缺陷是转化公式不满足统计学原理,并指出学分绩点测评的换算公式应建立在对特定学生群体特征参数群数值分析的基础上。以西南石油大学某专业学生某学年的成绩分布数据进行验证,采取统计原理、常数变异法和不动点原理进行建模,利用求导方法对学分绩点测评中的特征参数群进行层次划分,然后对每段划分积分,通过归一化来确定变异项,得出新的换算模型。结果表明新模型能更好地反映学生的学习成绩在综合素质测评中的地位。     

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).     

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.