A Bayesian method for classification and discrimination

We discuss Bayesian analyses of traditional normal-mixture models for classification and discrimination. The development involves application of an iterative resampling approach to Monte Carlo inference, commonly called Gibbs sampling, and demonstrates routine application. We stress the benefits of exact analyses over traditional classification and discrimination techniques, including the ease with which such analyses may be performed in a quite general setting, with possibly several normal-mixture components having different covariance matrices, the computation of exact posterior classification probabilities for observed data and for future cases to be classified, and posterior distributions for these probabilities that allow for assessment of second-level uncertainties in classification.     

随机变量投影的一种应用

利用随机变量的投影关系,定义了偏方差矩阵,从而导出了逆方差阵元素的一种形式在随机变量是正态的条件,它为判别条件独立性有方便的操作办法。     

Non uniform exponential bounds on normal approximation by Stein’s method and monotone size bias couplings

It is known that the normal approximation is applicable for sums of non negative random variables, W, with the commonly employed couplings. In this work, we use the Stein’s method to obtain a general theorem of non uniform exponential bound on normal approximation base on monotone size bias couplings of W. Applications of the main result to give the bound on normal approximation for binomial random variable, the number of bulbs on at the terminal time in the lightbulb process, and the number of m runs are also provided.     

Empirical Likelihood for the Additive Hazards Model with Current Status Data

We investigate empirical likelihood for the additive hazards model with current status data. An empirical log-likelihood ratio for a vector or subvector of regression parameters is defined and its limiting distribution is shown to be a standard chi-squared distribution. The proposed inference procedure enables us to make empirical likelihood-based inference for the regression parameters. Finite sample performance of the proposed method is assessed in simulation studies to compare with that of a normal approximation method, it shows that the empirical likelihood method provides more accurate inference than the normal approximation method. A real data example is used for illustration.     

Local influence diagnostics for incomplete overdispersed longitudinal counts

We develop local influence diagnostics to detect influential subjects when generalized linear mixed models are fitted to incomplete longitudinal overdispersed count data. The focus is on the influence stemming from the dropout model specification. In particular, the effect of small perturbations around an MAR specification are examined. The method is applied to data from a longitudinal clinical trial in epileptic patients. The effect on models allowing for overdispersion is contrasted with that on models that do not.     

高等师范语文教育环境初论

中国的师范教育已有百余年历史。语文教育是一切教育的基础之基础。语文素质的特殊性决定了语文教育的特殊性,语文教育的特殊性导致了语文师范教育环境的独特性。本文从语文教育独特的创新环境、语文学科研究能力和语文教学实践能力并举、强化综合素质教育的同时融入师范性教育、寻求语文教育中人文性教育和工具性教育的平衡点等方面对高等师范语文教育环境的特殊性进行探讨。     

Bayesian density regression for discrete outcomes

We develop Bayesian models for density regression with emphasis on discrete outcomes. The problem of density regression is approached by considering methods for multivariate density estimation of mixed scale variables, and obtaining conditional densities from the multivariate ones. The approach to multivariate mixed scale outcome density estimation that we describe represents discrete variables, either responses or covariates, as discretised versions of continuous latent variables. We present and compare several models for obtaining these thresholds in the challenging context of count data analysis where the response may be over‐ and/or under‐dispersed in some of the regions of the covariate space. We utilise a nonparametric mixture of multivariate Gaussians to model the directly observed and the latent continuous variables. The paper presents a Markov chain Monte Carlo algorithm for posterior sampling, sufficient conditions for weak consistency, and illustrations on density, mean and quantile regression utilising simulated and real datasets.     

新常态下中国经济的矛盾与出路

在经历了长期高速增长后,中国经济步入新的发展阶段.目前,经济下行压力增大,传统制造业产能严重过剩,货币宽松产生的大量流动性不仅没有有效流入实体经济,反倒推高了先行经济领域价格.随着人均收入水平提高,消费结构升级,供给结构与需求结构的矛盾加剧,要求加快产业结构调整,而在调结构中,又面临着多重经济悖论并存的局面.与此同时,国际经贸规则正在深刻变迁;美国强力推行遏制中国的亚太战略,推动制造业回流,加速了全球价值链重构,使进入中国市场的部分外商投资撤离风险上升;日本实行的安倍经济学效果存疑,英国脱欧加剧了欧盟的不稳定性,使欧盟经济进一步雪上加霜,日欧经济颓势短期无法改变.内外部经济环境的变化给中国带来新的挑战和机遇.中国要实现经济转型升级,跨越中等收入陷阱,必须统筹国内经济政策,实行政策之间的联动,通过供给侧改革,提升全要素生产率,增加有效供给,助推经济结构调整,完善开放型经济体制.     

高师院校环境工程专业学生职业技能训练的探索与实践

大力加强实验、实践教学改革是质量工程的重要内容,也是就业市场的客观要求。吉林师范大学借鉴教师教育专业学生职业技能训练的理念并参照职业院校学技能训练的模式,结合课程内容和就业市场,构建了具有我校人才培养特色的环境工程专业学生职业技能训练体系。通过几年的探索与实践,提高了学生工程实践和创新能力,得到了用人单位的好评。     

课堂双语教学实践的思考——以白城师范学院为例

双语教学是一种新的语言教学模式,在白城师范学院进行推广具有特殊的意义。本文从教学过程的七要素反思双语教学实践,为解决遇到的问题,分别从学院、教师、学生三个层面提出了相应的对策。