独立学院TEM4通过率及成因分析——以广州某独立学院为例

近五年的数据表明,独立学院英语专业四级考试通过率远远低于全国平均水平。调查发现,影响教学效果的三大主要因素——教师、学生、学习环境也是影响独立学院英语专业四级考试成绩的主要成因。文章旨在帮助读者了解独立学院英语专业基础教学阶段的教学现状和教学质量。     

生态认证产品生产者分析

通过描述性统计分析和列联表分析(卡方检验)对申请认证生态产品的生产者进行研究,发现:生产者申请生态认证的主要动机在于经济效益、市场需求,这种认证申请是理性的、可靠的;生产企业管理者在追求个人利益的同时,已明确认识到应通过生态认证提升企业形象,以维护生产者的市场利益;而采取有效措施规范认证市场、确保认证的实际效益,是当下亟待解决的问题。     

Conjugate Priors Represent Strong Pre-Experimental Assumptions

Abstract.  It is well known that Jeffreys' prior is asymptotically least favorable under the entropy risk, i.e. it asymptotically maximizes the mutual information between the sample and the parameter. However, in this paper we show that the prior that minimizes (subject to certain constraints) the mutual information between the sample and the parameter is natural conjugate when the model belongs to a natural exponential family. A conjugate prior can thus be regarded as maximally informative in the sense that it minimizes the weight of the observations on inferences about the parameter; in other words, the expected relative entropy between prior and posterior is minimized when a conjugate prior is used.     

因子分析在中学生人际信任分析中的应用

文章运用多元分析的方法,利用社会科学与数理统计的相关理论,对在成都市几所中学所做的484份人际信任问卷进行相关分析,研究了当前成都市中学生人际信任状况,认为同学们自我认同的亲疏关系是影响成都市中学生人际信任的重要因素,并就此提出了相关建议及对策。     

知识进化的要素、形式与规律

文章首先阐述了知识进化的要素:知识基因、知识DNA、知识细胞与知识体系。然后阐述了知识进化的形式。在此基础上,阐述了知识进化的规律。     

Some Faults of the Bayes Factor in Nonparametric Model Selection

We compare different Bayesian strategies for testing a parametric model versus a nonparametric alternative on the ground of their ability to solve the inconsistency problems arising when using the Bayes factor under certain conditions. A preliminary critical discussion of such an inconsistency is provided.     

Properties of bagged nearest neighbour classifiers

Summary.  It is shown that bagging, a computationally intensive method, asymptotically improves the performance of nearest neighbour classifiers provided that the resample size is less than 69% of the actual sample size, in the case of with-replacement bagging, or less than 50% of the sample size, for without-replacement bagging. However, for larger sampling fractions there is no asymptotic difference between the risk of the regular nearest neighbour classifier and its bagged version. In particular, neither achieves the large sample performance of the Bayes classifier. In contrast, when the sampling fractions converge to 0, but the resample sizes diverge to ∞, the bagged classifier converges to the optimal Bayes rule and its risk converges to the risk of the latter. These results are most readily seen when the two populations have well-defined densities, but they may also be derived in other cases, where densities exist in only a relative sense. Cross-validation can be used effectively to choose the sampling fraction. Numerical calculation is used to illustrate these theoretical properties.     

我国上市公司资本结构影响因素实证分析

文章运用因子分析、回归分析等多种定量分析方法,以我国部分上市公司的统计资料为基础,对影响资本结构的主要因素进行了实证分析,得到了我国上市公司的最佳资本结构区间以及影响我国上市公司资本结构的主要因素。     

A Bayesian decision theory approach to variable selection for discrimination

Motivated by examples in spectroscopy, we study variable selection for discrimination in problems with very many predictor variables. Assuming multivariate normal distributions with common variance for the predictor variables within groups, we develop a Bayesian decision theory approach that balances costs for variables against a loss due to classification errors. The approach is computationally intensive, requiring a simulation to approximate the intractable expected loss and a search, using simulated annealing, over a large space of possible subsets of variables. It is illustrated by application to a spectroscopic example with 3 groups, 100 variables, and 71 training cases, where the approach finds subsets of between 5 and 14 variables whose discriminatory power is comparable with that of linear discriminant analysis using principal components derived from the full 100 variables. We study both the evaluation of expected loss and the tuning of the simulated annealing for the example, and conclude that computational effort should be concentrated on the search.     

HIERARCHICAL BAYESIAN MODELLING OF SOCIAL VARIATION IN THE AGE DEPENDENCE OF DISABILITY PREVALENCE

Motivated by a study of social variation in the relationship of functional limitation prevalence to age, this paper examines methods for modelling social variation in health outcomes. It is argued that, from a Bayesian perspective, modelling the dependence of functional limitation prevalence on age separately for each social group, corresponds to an implausible prior model, in addition to leading to imprecise estimates for some groups. The alternative strategy of fitting a single model, perhaps including some age‐by‐group interactions but omitting higher‐order interactions, requires a strong prior commitment to the absence of such effects. Hierarchical Bayesian modelling is proposed as a compromise between these two analytical approaches. Under all hierarchical Bayes analyses there is strong evidence for an ethnic group difference in limitation prevalence in early‐ to mid‐adulthood among tertiary‐qualified males. In contrast, the single‐model approach largely misses this effect, while the group‐specific analyses exhibit an unrealistically large degree of heterogeneity in gender‐education‐specific ethnicity effects. The sensitivity of posterior inferences to prior specifications is studied.