教师课堂教学质量综合评价方法研究

教师课堂教学质量综合评价以课堂教学质量评价指标体系为基础,综合评价方法主要包括指标加权平均法和模糊综合评价法。难点是评价指标的设计和量化处理,权集的构造。对评价结果的分析可采用绝对评价(等级判断),相对评价,动态评价(个体差异的评价)。     

综合评价客观性的哲学探讨

综合评价在科学研究及社会经济中的应用日益广泛,各类评价经常见诸于媒体。同类评价由于不同机构选用的指标、权数及合成方法不同,评价结果大相径庭。由此,许多学者认为综合评价主观性强,客观性不足。本文从综合评价作为一种认知活动的本质出发,阐述综合评价的特点,进而讨论综合评价客体及评价标准所具有的客观性,论证综合评价的客观性。认识到综合评价的客观性,便于实际中促进综合评价方法与应用的发展。     

SPSS的因子分析在课堂教学质量评价中的应用

本文利用SPSS中的因子分析功能对本校学生评教数据进行分析,得出了具有一定参考价值的结果,并通过一个具体实例证实了因子分析在课堂教学质量评价中的可行性和必要性。     

国有商业银行市场营销效率的测算

现有的商业银行效率评价方法存在理论适用性,但也存在不同程度的局限性。文章从实现不同效率评价方法优势互补的目标出发,借鉴系统评价领域中组合评价的思想和方法,建立了基于漂移度的效率组合评价模型。并应用该模型对我国五大国有商业银行的市场营销效率进行了实证研究。研究结果证明了模型的科学性和有效性。     

对上市公司绩效的动态综合评价研究

对上市公司经营业绩进行评价是证券投资研究的一个重要方面。本文在建立上市公司的动态评价指标体系的基础上,运用动态综合评价方法,即“‘纵横向’拉开档次”法来确定评价指标在不同时刻的权重,并对我国石化行业上市公司的经营业绩进行了综合评价。     

综合评价结果的区分度与稳定性研究

综合评价是一项系统而又复杂的工作,其目的是获取能反映被评价对象全貌的综合评价值来区分不同个体的优劣。好的综合评价结果应该具备较高的区分度和稳定性,因此如何科学度量评价值的区分度和稳定性成为综合评价结果可靠与否的关键。文章构建一种新的区分度度量方法,提升了区分度的准确性和适用性;分析了导致综合评价结果波动的诸多因素,引进综合评价结果稳定性的度量指标,并证明了该指标的科学性与合理性;通过大量的数值模拟实验来计算不同综合评价方法的区分度和稳定性,研究区分度和稳定性的影响因素。结果表明,不同分布类型数据的取值规律不同,不同无量纲化方法处理数据的效用不同,不同综合评价方法确定权重的机理不同,任何一种因素的改变都会对综合评价结果的区分度和稳定性产生影响,所以进行综合评价前需要充分考虑数据的分布类型进而选择恰当的无量纲化方法和综合评价方法,以提高结果的区分度和稳定性。     

基于信息扩散原理的模糊综合评价模型

本文针对模糊综合评价模型中评价标准为不同等级,而每个等级分别由一个具体的数值点作为标志,且各评价指标又均具有惟一指标值的情况,利用信息扩散原理构建了评价矩阵,并以具体实例做了示范,从而使得在决策领域广泛使用的模糊综合评价模型的应用范围得到了拓展。     

评价的共识与共识评价的机制：基于子群的视角

本文针对群组评价中,忽视群组共识度高低程度而强行集成、忽视不同子群的潜在不可比而强行比较这两类问题,以共识度为主线,讨论子群评价的共识度测算方法、子群分类与构建、子群分歧的分解与识别等问题,并在此基础上,对如何提升群组评价共识程度的评价机制设计进行了思考,分别从事前、事后的角度给出了“信息反馈评价机制”、“加权修正评价机制”和“链式修正评价机制”的设计思路,为实现子群评价活动的合理开展提供了基本的研究框架。     

在科学发展观视野下构建社会评价指标体系

社会评价指标是衡量和评价社会发展状况和趋势的一种工具。在对国内外不同时期社会评价指标体系进行分析的基础上,在科学发展观视野和架构下,提出了社会评价指标体系的阶段性和发展性的原理,提出了适应中国现阶段和未来一个时期社会发展评价需要的新的社会评价指标体系。     

企业社会业绩评价的分类、原则与方法

促进企业履行社会责任需要有外部与内部两方面的努力,而无论是外部努力还是自身努力,及时、可靠的企业社会业绩信息都是必备的条件之一.为了更好地提供信息,有必要对企业社会业绩评价进行分类,根据目的不同,可以把企业社会业绩评价分为评优为导向的评价、打分为导向的评价、报告为导向的评价、管理为导向的评价等四种,不同类型的评价在评价原则和方法上相应地应该有所不同.     

Selection of tuning parameters in bridge regression models via Bayesian information criterion

We consider bridge regression models, which can produce a sparse or non-sparse model by controlling a tuning parameter in the penalty term. A crucial part of a model building strategy is the selection of the values for adjusted parameters, such as regularization and tuning parameters. Indeed, this can be viewed as a problem in selecting and evaluating the model. We propose a Bayesian selection criterion for evaluating bridge regression models. This criterion enables us to objectively select the values of the adjusted parameters. We investigate the effectiveness of our proposed modeling strategy with some numerical examples.     

Statistics at Liberal Arts Colleges

Statisticians and others who teach statistics at liberal arts colleges enjoy opportunities and encounter difficulties that are unique to the liberal arts setting. In July 1987 a small group of statisticians participated in a workshop at which discussion focused on three major issues: statistics in the liberal arts, the teaching of statistics, and the role of a statistician at a liberal arts college. By summarizing our discussion in this report we hope to provide support for statisticians at liberal arts colleges and to initiate discussion directed toward giving statistics education a prominent position in the liberal arts curriculum.     

A Spatial-temporal Model for Temperature with Seasonal Variance

We propose a spatial-temporal stochastic model for daily average surface temperature data. First, we build a model for a single spatial location, independently on the spatial information. The model includes trend, seasonality, and mean reversion, together with a seasonally dependent variance of the residuals. The spatial dependency is modelled by a Gaussian random field. Empirical fitting to data collected in 16 measurement stations in Lithuania over more than 40 years shows that our model captures the seasonality in the autocorrelation of the squared residuals, a property of temperature data already observed by other authors. We demonstrate through examples that our spatial-temporal model is applicable for prediction and classification.     

Bayesian modelling of health insurance losses

The purpose of this paper is to build a model for aggregate losses which constitutes a crucial step in evaluating premiums for health insurance systems. It aims at obtaining the predictive distribution of the aggregate loss within each age class of insured persons over the time horizon involved in planning employing the Bayesian methodology. The model proposed using the Bayesian approach is a generalization of the collective risk model, a commonly used model for analysing risk of an insurance system. Aggregate loss prediction is based on past information on size of loss, number of losses and size of population at risk. In modelling the frequency and severity of losses, the number of losses is assumed to follow a negative binomial distribution, individual loss sizes are independent and identically distributed exponential random variables, while the number of insured persons in a finite number of possible age groups is assumed to follow the multinomial distribution. Prediction of aggregate losses is based on the Gibbs sampling algorithm which incorporates the missing data approach.     

Meeting Student Needs for Multivariate Data Analysis: A Case Study in Teaching an Undergraduate Multivariate Data Analysis Course

Modern students encounter large, messy datasets long before setting foot in our classrooms. Many of these students need to develop skills in exploratory data analysis and multivariate analysis techniques for their jobs after college, but such topics are not covered in traditional introductory statistics courses. This case study describes my experience in designing and teaching an undergraduate course on multivariate data analysis with minimal prerequisites, using real data, active learning, and other interactive activities to help students tackle the material. Multivariate topics covered include clustering and classification (among others) for exploratory data analysis and an introduction to algorithmic modeling. Supplementary materials for this article are available online.     

Additive Regression Model for Continuous Time Processes

In the setting of additive regression model for continuous time process, we establish the optimal uniform convergence rates and optimal asymptotic quadratic error of additive regression. To build our estimate, we use the marginal integration method.     

对高职统计教学改革的再思考

本文针对高职统计教学培养的目标及存在的问题,从教学内容、教材、实践及师资等四个方面提出了改革的意见。     

True integer value time series

We construct an integer-valued stationary symmetric AR(1) process which can have either a positive or a negative lag-one autocorrelation. Nearly all integer-valued time series models are designed for observations which are non-negative integers or counts. They have innovations which are distributed on the non-negative integers and therefore obviously non-symmetric. We build our model using innovations that come from the difference of two independent identically distributed Poisson random variables. These innovations have a symmetric distribution, which has many advantages; in particular, they will allow us to model negative correlations. For our AR(1) process, we examine its basic properties and consider estimation via conditional least squares.     

Adaptive sequential design for regression on multi-resolution bases

This work investigates the problem of construction of designs for estimation and discrimination between competing linear models. In our framework, the unknown signal is observed with the addition of a noise and only a few evaluations of the noisy signal are available. The model selection is performed in a multi-resolution setting. In this setting, the locations of discrete sequential D and A designs are precisely constraint in a small number of explicit points. Hence, an efficient stochastic algorithm can be constructed that alternately improves the design and the model. Several numerical experiments illustrate the efficiency of our method for regression. One can also use this algorithm as a preliminary step to build response surfaces for sensitivity analysis.     

Inference in disease transmission experiments by using stochastic epidemic models

Summary.  The paper extends the susceptible–exposed–infective–removed model to handle heterogeneity introduced by spatially arranged populations, biologically plausible distributional assumptions and incorporation of observations from additional diagnostic tests. These extensions are motivated by a desire to analyse disease transmission experiments in a more detailed fashion than before. Such experiments are performed by veterinarians to gain knowledge about the dynamics of an infectious disease. By fitting our spatial susceptible–exposed–infective–removed with diagnostic testing model to data for a specific disease and production environment a valuable decision support tool is obtained, e.g. when evaluating on-farm control measures. Partial observability of the epidemic process is an inherent problem when trying to estimate model parameters from experimental data. We therefore extend existing work on Markov chain Monte Carlo estimation in partially observable epidemics to the multitype epidemic set-up of our model. Throughout the paper, data from a Belgian classical swine fever virus transmission experiment are used as a motivating example.